FCD_TorchIO.ipynb

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\n",
                "metadata": {
                  "needs_background": "light",
                  "tags": []
                },
                "output_type": "display_data",
                "text/plain": "<Figure size 1080x360 with 3 Axes>"
              }
            ]
          }
        },
        "dd93adc3872446d892bb2863ef9152ec": {
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          "model_name": "SliderStyleModel",
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          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "SliderStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": "",
            "handle_color": null
          }
        },
        "d2b943c6d4ce4b9284c2f2814f4da332": {
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          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hfW93WperjXW"
      },
      "source": [
        "# Automatic segmentation of Focal Cortical Dysplasia lesions using deep convolutional neural networks. \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yvxXp33F6yrW"
      },
      "source": [
        "# Config\n",
        "seed = 42  # for reproducibility\n",
        "training_split_ratio = 0.9  # use 90% of samples for training, 10% for testing\n",
        "num_epochs = 150"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AV1u9oKrMUR6",
        "outputId": "5d2edd84-1e65-430e-d2a6-2fd624e2b72d"
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Thu Nov 12 13:45:28 2020       \r\n",
            "+-----------------------------------------------------------------------------+\r\n",
            "| NVIDIA-SMI 410.104      Driver Version: 410.104      CUDA Version: 10.1     |\r\n",
            "|-------------------------------+----------------------+----------------------+\r\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\r\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\r\n",
            "|===============================+======================+======================|\r\n",
            "|   0  Tesla V100-DGXS...  On   | 00000000:0F:00.0 Off |                    0 |\r\n",
            "| N/A   45C    P0    58W / 300W |      0MiB / 32478MiB |      0%      Default |\r\n",
            "+-------------------------------+----------------------+----------------------+\r\n",
            "                                                                               \r\n",
            "+-----------------------------------------------------------------------------+\r\n",
            "| Processes:                                                       GPU Memory |\r\n",
            "|  GPU       PID   Type   Process name                             Usage      |\r\n",
            "|=============================================================================|\r\n",
            "|  No running processes found                                                 |\r\n",
            "+-----------------------------------------------------------------------------+\r\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_2l6KTwb66HF"
      },
      "source": [
        "# !pip install --quiet --upgrade pip\n",
        "# !pip install --quiet --upgrade niwidgets\n",
        "# !pip install --quiet --upgrade highresnet\n",
        "# !pip install --quiet --upgrade unet\n",
        "# !pip install --quiet --upgrade torchio\n",
        "# !pip install tqdm -U\n",
        "#!apt-get -qq install tree"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UzQT58inOpqf"
      },
      "source": [
        "## **Import dependencies**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-ZcCP8Kt664C",
        "outputId": "fb3a669d-e64e-44ec-fbf6-b9ab9c66a1e2"
      },
      "source": [
        "import copy\n",
        "import enum\n",
        "import random; random.seed(seed)\n",
        "import warnings\n",
        "import tempfile\n",
        "import subprocess\n",
        "import multiprocessing\n",
        "from pathlib import Path\n",
        "\n",
        "import torch\n",
        "import torch.nn.functional as F\n",
        "from torchvision.utils import make_grid, save_image\n",
        "torch.manual_seed(seed)\n",
        "\n",
        "import torchio as tio\n",
        "from torchio import AFFINE, DATA\n",
        "\n",
        "import numpy as np\n",
        "import nibabel as nib\n",
        "from unet import UNet\n",
        "from scipy import stats\n",
        "import SimpleITK as sitk\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from IPython import display\n",
        "from tqdm.notebook import tqdm\n",
        "\n",
        "print('TorchIO version:', tio.__version__)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "If you use TorchIO for your research, please cite the following paper:\n",
            "Pérez-García et al., TorchIO: a Python library for efficient loading,\n",
            "preprocessing, augmentation and patch-based sampling of medical images\n",
            "in deep learning. Credits instructions: https://torchio.readthedocs.io/#credits\n",
            "\n",
            "TorchIO version: 0.17.51\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c9FLabY-Oz6L"
      },
      "source": [
        "## *Defining helper functions for loading and displaying images*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8uloxQef68yL"
      },
      "source": [
        "def show_nifti(image_path_or_image, colormap='gray'):\n",
        "    try:\n",
        "        from niwidgets import NiftiWidget\n",
        "        with warnings.catch_warnings():\n",
        "            warnings.filterwarnings(\"ignore\", category=FutureWarning)\n",
        "            widget = NiftiWidget(image_path_or_image)\n",
        "            widget.nifti_plotter(colormap=colormap)\n",
        "    except Exception:\n",
        "        if isinstance(image_path_or_image, nib.AnalyzeImage):\n",
        "            nii = image_path_or_image\n",
        "        else:\n",
        "            image_path = image_path_or_image\n",
        "            nii = nib.load(str(image_path))\n",
        "        k = int(nii.shape[-1] / 2)\n",
        "        plt.imshow(nii.dataobj[..., k], cmap=colormap)\n",
        "\n",
        "def show_subject(subject, image_name, label_name=None):\n",
        "    if label_name is not None:\n",
        "        subject = copy.deepcopy(subject)\n",
        "        affine = subject[label_name].affine\n",
        "        label_image = subject[label_name].as_sitk()\n",
        "        label_image = sitk.Cast(label_image, sitk.sitkUInt8)\n",
        "        border = sitk.BinaryContour(label_image)\n",
        "        border_array, _ = tio.utils.sitk_to_nib(border)\n",
        "        border_tensor = torch.from_numpy(border_array)[0]\n",
        "        image_tensor = subject[image_name].data[0]\n",
        "        image_tensor[border_tensor > 0.5] = image_tensor.max()\n",
        "    with tempfile.NamedTemporaryFile(suffix='.nii') as f:\n",
        "        subject[image_name].save(f.name)\n",
        "        show_nifti(f.name)\n",
        "\n",
        "def plot_histogram(axis, tensor, num_positions=100, label=None, alpha=0.05, color=None):\n",
        "    values = tensor.numpy().ravel()\n",
        "    kernel = stats.gaussian_kde(values)\n",
        "    positions = np.linspace(values.min(), values.max(), num=num_positions)\n",
        "    histogram = kernel(positions)\n",
        "    kwargs = dict(linewidth=1, color='black' if color is None else color, alpha=alpha)\n",
        "    if label is not None:\n",
        "        kwargs['label'] = label\n",
        "    axis.plot(positions, histogram, **kwargs)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dpOI7TgCMUSA",
        "outputId": "2e03af66-e4aa-4f38-bed8-5837652a991e"
      },
      "source": [
        "!pwd"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/workspace/sharad/Final\r\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SIvz6rgx7apr",
        "outputId": "27f17ec8-247d-4d9d-efe4-53f7279146bb"
      },
      "source": [
        "directory = Path('/workspace/sharad/Final/Fcd_Data')\n",
        "images_dir = directory / 'images'\n",
        "inter_dir = directory / 'Interhemi'\n",
        "pvms_dir = directory / 'Pvms'\n",
        "labels_dir = directory / 'Labels_new'\n",
        "\n",
        "images_paths = sorted(images_dir.glob('*nii.gz'))\n",
        "inter_paths = sorted(inter_dir.glob('*nii.gz'))\n",
        "pvms_paths = sorted(pvms_dir.glob('*nii.gz'))\n",
        "labels_paths = sorted(labels_dir.glob('*nii.gz'))\n",
        "\n",
        "subjects = []\n",
        "\n",
        "for (images_path,inter_path,pvms_path,labels_path) in zip(images_paths,inter_paths,pvms_paths,labels_paths):\n",
        "  \n",
        "  subject = tio.Subject(\n",
        "        mri=tio.ScalarImage(images_path),\n",
        "        inter=tio.ScalarImage(inter_path),\n",
        "        pvm=tio.ScalarImage(pvms_path),\n",
        "        label=tio.LabelMap(labels_path),\n",
        "    )\n",
        "  subjects.append(subject)\n",
        "\n",
        "dataset = tio.SubjectsDataset(subjects)\n",
        "print('Dataset size:', len(dataset), 'subjects')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dataset size: 20 subjects\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mB9jMKxUO9ee"
      },
      "source": [
        "## Sanity check: Display image and label for one subject "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457,
          "referenced_widgets": [
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            "105a00f4260742a1a62b96648548ceab",
            "dd93adc3872446d892bb2863ef9152ec",
            "d2b943c6d4ce4b9284c2f2814f4da332",
            "4f178586437a4a30b097fffbc578b996",
            "9a19641a983c4d7dba273d1f60f7ff7d",
            "6b9155de7f68406aaa721f748523d3dd",
            "cc92376d8c934defb1508c886c6b2e41",
            "330729e7781148a7b11cd1ac79541912",
            "7dbfc67864fa4692875d382ab7037484"
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        },
        "id": "peLR6aMW7vb7",
        "outputId": "74dd38dd-b66a-40ea-bdb2-bd7ce4295cc7"
      },
      "source": [
        "one_subject = dataset[2]\n",
        "print(one_subject)\n",
        "print(one_subject.mri)\n",
        "show_subject(tio.ToCanonical()(one_subject), 'mri', label_name='label')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Subject(Keys: ('mri', 'inter', 'pvm', 'label'); images: 4)\n",
            "ScalarImage(shape: (1, 256, 256, 192); spacing: (0.92, 0.92, 0.94); orientation: RAS+; memory: 48.0 MiB; type: intensity)\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7dbfc67864fa4692875d382ab7037484",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "interactive(children=(IntSlider(value=127, continuous_update=False, description='x', max=255), IntSlider(value…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4pbLzjN5HGTS",
        "outputId": "097e678a-33cb-48ad-ec47-63a197f98f35"
      },
      "source": [
        "num_subjects = len(dataset)\n",
        "num_training_subjects = 16\n",
        "\n",
        "training_subjects = subjects[:num_training_subjects]\n",
        "validation_subjects = subjects[num_training_subjects:-2]\n",
        "testing_subjects = subjects[num_training_subjects+2:]\n",
        "\n",
        "training_set = tio.SubjectsDataset(\n",
        "    training_subjects)\n",
        "\n",
        "validation_set = tio.SubjectsDataset(\n",
        "    validation_subjects)\n",
        "\n",
        "testing_set = tio.SubjectsDataset(testing_subjects)\n",
        "\n",
        "print('Training set:', len(training_set), 'subjects')\n",
        "\n",
        "print('Validation set:', len(validation_set), 'subjects')\n",
        "\n",
        "print('Testing set:', len(testing_set), 'subjects')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training set: 16 subjects\n",
            "Validation set: 2 subjects\n",
            "Testing set: 2 subjects\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HyRyuL2nPoLC"
      },
      "source": [
        "## Defining more functions that perform the following:\n",
        "\n",
        "1. Define functions for computing loss and defining hyperparameters.\n",
        "2. Load the MR images by batch and concatenate all modalities.\n",
        "3. Define function to run 1 epoch using 1 batch of images during training.\n",
        "4. Define Training sequence."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "53Y0Ud4kHuxk"
      },
      "source": [
        "device = torch.device('cuda') if torch.cuda.is_available() else 'cpu'\n",
        "CHANNELS_DIMENSION = 1\n",
        "SPATIAL_DIMENSIONS = 2, 3, 4\n",
        "\n",
        "class Action(enum.Enum):\n",
        "    TRAIN = 'Training'\n",
        "    VALIDATE = 'Validation'\n",
        "\n",
        "def prepare_batch(batch, device):\n",
        "    imgs = batch['mri'][DATA]\n",
        "    intermaps = batch['inter'][DATA]\n",
        "    pvmmaps = batch['pvm'][DATA]\n",
        "    inputs = torch.cat([imgs,intermaps,pvmmaps],dim = 1).to(device)\n",
        "    foreground = batch['label'][DATA].to(device)\n",
        "    background = 1 - foreground\n",
        "    targets = foreground\n",
        "    #targets = torch.cat((background, foreground), dim=CHANNELS_DIMENSION)\n",
        "    return inputs, targets\n",
        "\n",
        "def get_dice_score(output, target, epsilon=1e-9):\n",
        "    p0 = output\n",
        "    g0 = target\n",
        "    p1 = 1 - p0\n",
        "    g1 = 1 - g0\n",
        "    tp = (p0 * g0).sum(dim=SPATIAL_DIMENSIONS)\n",
        "    fp = (p0 * g1).sum(dim=SPATIAL_DIMENSIONS)\n",
        "    fn = (p1 * g0).sum(dim=SPATIAL_DIMENSIONS)\n",
        "    num = 2 * tp\n",
        "    denom = 2 * tp + fp + fn + epsilon\n",
        "    dice_score = num / denom\n",
        "    return dice_score\n",
        "\n",
        "def get_dice_loss(output, target):\n",
        "    return 1 - get_dice_score(output, target)\n",
        "\n",
        "def forward(model, inputs):\n",
        "    with warnings.catch_warnings():\n",
        "        warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
        "        logits = model(inputs)\n",
        "    return logits\n",
        "\n",
        "def get_model_and_optimizer(device):\n",
        "    model = UNet(\n",
        "        in_channels=3,\n",
        "        out_classes=2,\n",
        "        dimensions=3,\n",
        "        num_encoding_blocks=6,\n",
        "        out_channels_first_layer=8,\n",
        "        normalization='batch',\n",
        "        upsampling_type='linear',\n",
        "        padding=True,\n",
        "        activation='PReLU',\n",
        "    ).to(device)\n",
        "    optimizer = torch.optim.AdamW(model.parameters())\n",
        "    return model, optimizer\n",
        "\n",
        "def run_epoch(epoch_idx, action, loader, model, optimizer):\n",
        "    is_training = action == Action.TRAIN\n",
        "    epoch_losses = []\n",
        "    model.train(is_training)\n",
        "    for batch_idx, batch in enumerate(tqdm(loader)):\n",
        "        inputs, targets = prepare_batch(batch, device)\n",
        "        optimizer.zero_grad()\n",
        "        with torch.set_grad_enabled(is_training):\n",
        "            logits = forward(model, inputs)\n",
        "            probabilities = F.softmax(logits, dim=CHANNELS_DIMENSION)\n",
        "            dice = get_dice_score(probabilities, targets)\n",
        "            dice_v = dice.mean()\n",
        "            batch_losses = get_dice_loss(probabilities, targets)\n",
        "            batch_loss = batch_losses.mean()\n",
        "            if is_training:\n",
        "                batch_loss.backward()\n",
        "                optimizer.step()\n",
        "            epoch_losses.append(batch_loss.item())\n",
        "    epoch_losses = np.array(epoch_losses)\n",
        "    print(f'{action.value} mean loss: {epoch_losses.mean():0.3f} dice value: {dice_v.mean():0.3f}')\n",
        "\n",
        "def train(num_epochs, training_loader, validation_loader, model, optimizer, weights_stem):\n",
        "    #run_epoch(0, Action.VALIDATE, validation_loader, model, optimizer)\n",
        "    for epoch_idx in range(1, num_epochs + 1):\n",
        "        print('Starting epoch ', epoch_idx)\n",
        "        run_epoch(epoch_idx, Action.TRAIN, training_loader, model, optimizer)\n",
        "        run_epoch(epoch_idx, Action.VALIDATE, validation_loader, model, optimizer)\n",
        "        torch.save(model.state_dict(), f'{weights_stem}_epoch_{epoch_idx}.pth')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "szohoTsBptxS"
      },
      "source": [
        "## Define TorchIO image loading queue\n",
        "\n",
        "For more information about using Queues refer [this documentation](https://torchio.readthedocs.io/data/patch_training.html#queue)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pOqPHpQ1OOMy"
      },
      "source": [
        "Ttraining_batch_size = 100\n",
        "validation_batch_size = 2 * training_batch_size\n",
        "\n",
        "patch_size = 32\n",
        "samples_per_volume = 40\n",
        "max_queue_length = 800\n",
        "\n",
        "patches_training_set = tio.Queue(\n",
        "    subjects_dataset=training_set,\n",
        "    max_length=max_queue_length,\n",
        "    samples_per_volume=samples_per_volume,\n",
        "    sampler=tio.sampler.LabelSampler(patch_size, 'label', {0:1,1:1}),\n",
        "    num_workers=0,\n",
        "    shuffle_subjects=True,\n",
        "    shuffle_patches=True,\n",
        ")\n",
        "\n",
        "patches_validation_set = tio.Queue(\n",
        "    subjects_dataset=validation_set,\n",
        "    max_length=max_queue_length,\n",
        "    samples_per_volume=samples_per_volume,\n",
        "    sampler=tio.sampler.LabelSampler(patch_size, 'label',{0:1,1:1}),\n",
        "    num_workers=0,\n",
        "    shuffle_subjects=False,\n",
        "    shuffle_patches=False,\n",
        ")\n",
        "\n",
        "training_loader = torch.utils.data.DataLoader(\n",
        "    patches_training_set, batch_size=training_batch_size)\n",
        "\n",
        "validation_loader = torch.utils.data.DataLoader(\n",
        "    patches_validation_set, batch_size=validation_batch_size)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r-lHzbHKqYqi"
      },
      "source": [
        "## Visualizing patches created for one batch"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sKpN0I1SO-Lq",
        "scrolled": false,
        "outputId": "f2435f18-1186-4bd0-b432-a134dc64a44b"
      },
      "source": [
        "one_batch = next(iter(training_loader))\n",
        "k = int(patch_size // 8)\n",
        "batch_mri = one_batch['mri'][DATA][..., k]\n",
        "batch_label = one_batch['label'][DATA][..., k]\n",
        "slices = torch.cat((batch_mri, batch_label))\n",
        "image_path = 'batch_patches.png'\n",
        "save_image(slices, image_path, nrow=training_batch_size, normalize=True, scale_each=True)\n",
        "display.Image(image_path)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v7-bQNKPqlEY"
      },
      "source": [
        "## Start the training sequence"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K2Zb15ooPGKv",
        "scrolled": false
      },
      "source": [
        "model, optimizer = get_model_and_optimizer(device)\n",
        "\n",
        "weights_stem = 'patches'\n",
        "train(num_epochs, training_loader, validation_loader, model, optimizer, weights_stem)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "51dH57Kwqr22"
      },
      "source": [
        "## Inference\n",
        " 1. Run inference sequence on the test set (performed by batch)\n",
        " 2. Aggregate the output patches to reconstruct the full image (Reference is the T1/T2-FLAIR volume space)\n",
        " 3. Convert to Nifti format and save the file"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UREnr_SSCWh0",
        "scrolled": false,
        "colab": {
          "referenced_widgets": [
            "f69ecf0dec864857a67f59e18e436788"
          ]
        },
        "outputId": "bfabfa28-824d-42a0-99ff-d9a40f181296"
      },
      "source": [
        "sample = testing_set[0]\n",
        "input_tensor = torch.zeros_like(sample.mri.data[0])\n",
        "patch_size = 32  # we can user larger patches for inference\n",
        "patch_overlap = 4, 4, 4\n",
        "grid_sampler = tio.inference.GridSampler(\n",
        "    sample,\n",
        "    patch_size,\n",
        "    patch_overlap,\n",
        ")\n",
        "patch_loader = torch.utils.data.DataLoader(\n",
        "    grid_sampler, batch_size=validation_batch_size)\n",
        "aggregator = tio.inference.GridAggregator(grid_sampler)\n",
        "\n",
        "model.eval()\n",
        "with torch.no_grad():\n",
        "    for patches_batch in patch_loader:\n",
        "        imgs = patches_batch['mri'][DATA]\n",
        "        intermaps = patches_batch['inter'][DATA]\n",
        "        pvmmaps = patches_batch['pvm'][DATA]\n",
        "        inputs = torch.cat([imgs,intermaps,pvmmaps],dim = 1).to(device)\n",
        "        locations = patches_batch[tio.LOCATION]\n",
        "        logits = model(inputs)\n",
        "        labels = logits.argmax(dim=CHANNELS_DIMENSION, keepdim=True)\n",
        "        aggregator.add_batch(labels, locations)\n",
        "\n",
        "foreground = aggregator.get_output_tensor()\n",
        "affine = sample.mri.affine\n",
        "\n",
        "label_image = tio.utils.nib_to_sitk(foreground, affine)\n",
        "\n",
        "border_array, _ = tio.utils.sitk_to_nib(label_image)\n",
        "input_tensor[border_array > 0.5] = 1\n",
        "nii = nib.Nifti1Image(input_tensor.numpy(), affine)\n",
        "show_nifti(nii)\n",
        "#nib.save(nii, 'out2.nii.gz')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:3121: UserWarning: Default upsampling behavior when mode=trilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.\n",
            "  \"See the documentation of nn.Upsample for details.\".format(mode))\n",
            "/usr/local/lib/python3.6/dist-packages/torchio/data/inference/aggregator.py:159: UserWarning: Medical image frameworks such as ITK do not support int64. Casting to int32...\n",
            "  warnings.warn(message)\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f69ecf0dec864857a67f59e18e436788",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "interactive(children=(IntSlider(value=84, continuous_update=False, description='x', max=169), IntSlider(value=…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bBigCi1zMUSL",
        "outputId": "2613894b-35d4-4467-911b-dea9db5760e4"
      },
      "source": [
        "sample.label.plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D1_BN3PcMUSM"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}