example_colab.ipynb

{
  "cells": [
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Libraries"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Current active folder"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v1ziaYhC1kE8",
        "outputId": "ff8a5325-46f0-4b8c-e8fa-86b2c0550ae0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content\n"
          ]
        }
      ],
      "source": [
        "!pwd"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Clone ICI loss repo"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dyRGFiIGuo_e",
        "outputId": "d2728633-7e45-429f-b7d5-e8ac45be017e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Cloning into 'ICI-loss'...\n",
            "remote: Enumerating objects: 30, done.\u001b[K\n",
            "remote: Counting objects: 100% (30/30), done.\u001b[K\n",
            "remote: Compressing objects: 100% (22/22), done.\u001b[K\n",
            "remote: Total 30 (delta 12), reused 23 (delta 8), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (30/30), 189.37 KiB | 4.98 MiB/s, done.\n",
            "/content/ICI-loss\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/BrainImageAnalysis/ICI-loss.git\n",
        "%cd /content/ICI-loss/\n",
        "# !git pull\n",
        "# !rm -rf /content/ICI-loss/\n",
        "\n",
        "import sys\n",
        "sys.path.insert(0,'/content/ICI-loss/')"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Install requirements"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AN8r_Mi42ehI",
        "outputId": "bb8f6dd4-1324-49a2-e878-77d795bc90f0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting monai\n",
            "  Downloading monai-1.1.0-202212191849-py3-none-any.whl (1.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.2/1.2 MB\u001b[0m \u001b[31m25.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: torch>=1.8 in /usr/local/lib/python3.9/dist-packages (from monai) (2.0.0+cu118)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.9/dist-packages (from monai) (1.22.4)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.9/dist-packages (from torch>=1.8->monai) (3.10.7)\n",
            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.9/dist-packages (from torch>=1.8->monai) (2.0.0)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.9/dist-packages (from torch>=1.8->monai) (3.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.9/dist-packages (from torch>=1.8->monai) (1.11.1)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.9/dist-packages (from torch>=1.8->monai) (4.5.0)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.9/dist-packages (from torch>=1.8->monai) (3.1.2)\n",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.9/dist-packages (from triton==2.0.0->torch>=1.8->monai) (3.25.2)\n",
            "Requirement already satisfied: lit in /usr/local/lib/python3.9/dist-packages (from triton==2.0.0->torch>=1.8->monai) (16.0.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.9/dist-packages (from jinja2->torch>=1.8->monai) (2.1.2)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.9/dist-packages (from sympy->torch>=1.8->monai) (1.3.0)\n",
            "Installing collected packages: monai\n",
            "Successfully installed monai-1.1.0\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.9/dist-packages (2.0.0+cu118)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.9/dist-packages (0.15.1+cu118)\n",
            "Requirement already satisfied: torchaudio in /usr/local/lib/python3.9/dist-packages (2.0.1+cu118)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.9/dist-packages (from torch) (3.10.7)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.9/dist-packages (from torch) (3.0)\n",
            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.9/dist-packages (from torch) (2.0.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.9/dist-packages (from torch) (4.5.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.9/dist-packages (from torch) (1.11.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.9/dist-packages (from torch) (3.1.2)\n",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.9/dist-packages (from triton==2.0.0->torch) (3.25.2)\n",
            "Requirement already satisfied: lit in /usr/local/lib/python3.9/dist-packages (from triton==2.0.0->torch) (16.0.0)\n",
            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.9/dist-packages (from torchvision) (8.4.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.9/dist-packages (from torchvision) (2.27.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.9/dist-packages (from torchvision) (1.22.4)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.9/dist-packages (from jinja2->torch) (2.1.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/dist-packages (from requests->torchvision) (3.4)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.9/dist-packages (from requests->torchvision) (2.0.12)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests->torchvision) (2022.12.7)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from requests->torchvision) (1.26.15)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.9/dist-packages (from sympy->torch) (1.3.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install monai\n",
        "!pip3 install torch torchvision torchaudio"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Import libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zbbFx7YMt2bx",
        "outputId": "b508074b-7f2d-4f6a-ccdc-0c369adce5e2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2.0.0+cu118\n",
            "1\n",
            "0\n",
            "<torch.cuda.device object at 0x7efc8f8d8910>\n",
            "Tesla T4\n",
            "OK\n"
          ]
        }
      ],
      "source": [
        "import nibabel as nib\n",
        "import torch\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "from losses.ICI_loss import ICILoss\n",
        "from losses.dICI_loss import dICILoss\n",
        "from losses import tools\n",
        "\n",
        "from monai.losses import DiceLoss\n",
        "\n",
        "print(torch.__version__)\n",
        "print(torch.cuda.device_count())\n",
        "print(torch.cuda.current_device())\n",
        "print(torch.cuda.device(0))\n",
        "print(torch.cuda.get_device_name(0))\n",
        "print(\"OK\")"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Load example data"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Example data (i.e. used in the paper) are located inside folder `example_blobs`. Please change the folder's location accordingly."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BXF7RRPJt2by",
        "outputId": "69fe6c34-5135-46ef-823b-502a6b734c87"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "torch.Size([1, 1, 197, 233, 189])\n",
            "torch.Size([1, 1, 197, 233, 189])\n"
          ]
        }
      ],
      "source": [
        "nii_img = nib.load(\"/content/ICI-loss/example_blobs/label-0.nii.gz\")\n",
        "nii_img_tensor = torch.from_numpy(nii_img.get_fdata())\n",
        "x, y, z = nii_img_tensor.shape\n",
        "nii_img_tensor_label = torch.reshape(nii_img_tensor, (1, 1, x, y, z))\n",
        "print(nii_img_tensor_label.shape)\n",
        "\n",
        "nii_img = nib.load(\"/content/ICI-loss/example_blobs/output-0.nii.gz\")\n",
        "nii_img_tensor = torch.from_numpy(nii_img.get_fdata())\n",
        "x, y, z = nii_img_tensor.shape\n",
        "nii_img_tensor_output = torch.reshape(nii_img_tensor, (1, 1, x, y, z))\n",
        "print(nii_img_tensor_output.shape)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Show the example data (label vs. output)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "id": "KqaPsBG9t2bz",
        "outputId": "52704d5f-a30f-4ac4-ed27-da7313fb5dbf"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "image = nii_img_tensor_label[0, 0, :, :, 0]\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.imshow(image)\n",
        "plt.title(\"Label instances\")\n",
        "\n",
        "image = nii_img_tensor_output[0, 0, :, :, 0]\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.imshow(image)\n",
        "plt.title(\"Output instances\")\n",
        "plt.show()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## How to use ICI and dICI losses"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Make an instance of ICILoss class. It can be used together with any pixel-wise segmentation losses (e.g. Dice loss, Focal loss, etc.). In this example, losses from MONAI core library are used. Note that the ICILoss have a lot of parameters.\n",
        "\n",
        "IMPORTANT NOTE: Because the toy data are already binary, we do not need any non-linear function. Please change accordingly to your data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rQLmw2Wyt2bz",
        "outputId": "2a0f1bba-d376-4373-98dc-029e4781c55a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "VALUES OF PARAMETERS\n",
            " -> loss_function_pixel: DiceLoss()\n",
            " -> loss_function_instance: DiceLoss()\n",
            " -> loss_function_center: DiceLoss()\n",
            " -> num_out_chn: 1\n",
            " -> object_chn: 1\n",
            " -> spatial_dims: 3\n",
            " -> activation: none\n",
            " -> reduce: mean\n",
            " -> instance_wise_reduce: instance\n",
            " -> num_iterations: 250\n",
            " -> max_cc_out: 50\n",
            " -> min_instance_size: 0\n",
            " -> segmentation_threshold: 0.5\n",
            " -> centroid_offset: 3\n",
            " -> mul_too_many: 50\n",
            " -> smoother: 1e-07\n",
            " -> instance_wise_loss_no_tp: True\n",
            " -> weighted_instance_wise: False\n",
            " -> weighted_fdr: False\n",
            " -> rate_instead_number: False\n"
          ]
        }
      ],
      "source": [
        "## IMPORTANT NOTE: Because the toy data are already binary, we do not need any non-linear function.\n",
        "## Please read help(ICILoss) for full explanation (i.e. parameters) of the ICI loss.\n",
        "\n",
        "loss_dice = DiceLoss(\n",
        "    to_onehot_y=False, \n",
        "    sigmoid=False, \n",
        "    softmax=False\n",
        "    )\n",
        "\n",
        "loss_dice_center = DiceLoss(\n",
        "    to_onehot_y=False, \n",
        "    sigmoid=False, \n",
        "    softmax=False\n",
        "    )\n",
        "\n",
        "activation=\"none\"\n",
        "num_out_chn = 1\n",
        "object_chn = 1\n",
        "mul_too_many = 50\n",
        "centroid_offset = 3\n",
        "num_iterations = 250\n",
        "max_false_detections = 50\n",
        "rate_instead_number = False\n",
        "\n",
        "ici_loss_function = ICILoss(\n",
        "    loss_function_pixel=loss_dice,\n",
        "    loss_function_instance=loss_dice_center,\n",
        "    loss_function_center=loss_dice_center,\n",
        "    activation=activation,\n",
        "    num_out_chn=num_out_chn,\n",
        "    object_chn=object_chn,\n",
        "    mul_too_many=mul_too_many,\n",
        "    max_cc_out=max_false_detections,\n",
        "    num_iterations=num_iterations,\n",
        "    centroid_offset=centroid_offset,\n",
        "    rate_instead_number=rate_instead_number,\n",
        "    instance_wise_loss_no_tp=True,\n",
        ")\n",
        "\n",
        "ici_loss_function.print_parameters()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Calculate ICI loss"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z4I_dZ9st2b0",
        "outputId": "c6ab811c-f90e-40f8-86b7-9fc4c2b1b9e2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dice loss value:  tensor(0.4587, device='cuda:0', dtype=torch.float64, grad_fn=<MeanBackward0>)\n",
            "\n",
            "ICI Loss\n",
            "seg_pixel (dice) : tensor(0.4587, device='cuda:0', dtype=torch.float64, grad_fn=<MeanBackward0>)  - with gradients?  True\n",
            "seg_instance     : tensor(0.4786, device='cuda:0', dtype=torch.float64, grad_fn=<MeanBackward0>)  - with gradients?  True\n",
            "seg_center       : tensor(0.5793, device='cuda:0', dtype=torch.float64, grad_fn=<MeanBackward0>)  - with gradients?  True\n",
            "seg_false        : tensor(0.5056, device='cuda:0', dtype=torch.float64, grad_fn=<MeanBackward0>)  - with gradients?  True\n",
            "num false        : tensor(2., device='cuda:0', dtype=torch.float64, grad_fn=<MeanBackward0>)  - with gradients?  True\n",
            "num missed       : tensor(1., device='cuda:0', dtype=torch.float64, grad_fn=<MeanBackward0>)  - with gradients?  True\n",
            "DONE!\n"
          ]
        }
      ],
      "source": [
        "outputs = nii_img_tensor_output.clone().detach().requires_grad_(True).to(\"cuda:0\")\n",
        "labels = nii_img_tensor_label.to(\"cuda:0\")\n",
        "\n",
        "## We can play with the data here (i.e. change the numbers of instances in labels/outputs to 0)\n",
        "# outputs = outputs * 0.\n",
        "# labels = labels * 0.\n",
        "\n",
        "dice_loss = loss_dice(outputs, labels)\n",
        "print(\"Dice loss value: \", dice_loss)\n",
        "\n",
        "seg_pixel, seg_instance, seg_center, seg_fdr, cc_falsed, cc_missed = ici_loss_function(\n",
        "    outputs, # outputs, zeros\n",
        "    labels,  # labels, ones\n",
        ")\n",
        "\n",
        "print(\"\\nICI Loss\")\n",
        "print(\"seg_pixel (dice) :\", seg_pixel, \" - with gradients? \", seg_pixel.requires_grad)\n",
        "print(\"seg_instance     :\", seg_instance, \" - with gradients? \", seg_instance.requires_grad)\n",
        "print(\"seg_center       :\", seg_center, \" - with gradients? \", seg_center.requires_grad)\n",
        "print(\"seg_false        :\", seg_fdr, \" - with gradients? \", seg_fdr.requires_grad)\n",
        "print(\"num false        :\", cc_falsed, \" - with gradients? \", cc_falsed.requires_grad)\n",
        "print(\"num missed       :\", cc_missed, \" - with gradients? \", cc_missed.requires_grad)\n",
        "print(\"DONE!\")"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Make an instance of dICILoss class and calculate the loss."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fdvy4uuUt2b0",
        "outputId": "a0027ca0-106c-421d-cb3c-d0343b6cb211"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "dICI Loss\n",
            "seg_pixel (dice) : tensor(0.4587, device='cuda:0', dtype=torch.float64, grad_fn=<MeanBackward0>)  - with gradients?  True\n",
            "seg_instance     : tensor(0.4786, device='cuda:0', dtype=torch.float64, grad_fn=<MeanBackward0>)  - with gradients?  True\n",
            "seg_instance_out : tensor(0.6545, device='cuda:0', dtype=torch.float64, grad_fn=<MeanBackward0>)  - with gradients?  True\n",
            "seg_center       : tensor(0.5793, device='cuda:0', dtype=torch.float64, grad_fn=<MeanBackward0>)  - with gradients?  True\n",
            "seg_false        : tensor(0.5056, device='cuda:0', dtype=torch.float64, grad_fn=<MeanBackward0>)  - with gradients?  True\n",
            "num false        : tensor(2., device='cuda:0', dtype=torch.float64, grad_fn=<MeanBackward0>)  - with gradients?  True\n",
            "num missed       : tensor(1., device='cuda:0', dtype=torch.float64, grad_fn=<MeanBackward0>)  - with gradients?  True\n",
            "DONE!\n"
          ]
        }
      ],
      "source": [
        "dici_loss_function = dICILoss(\n",
        "    loss_function_pixel=loss_dice,\n",
        "    loss_function_instance=loss_dice_center,\n",
        "    loss_function_center=loss_dice_center,\n",
        "    activation=activation,\n",
        "    num_out_chn=num_out_chn,\n",
        "    object_chn=object_chn,\n",
        "    mul_too_many=mul_too_many,\n",
        "    max_cc_out=max_false_detections,\n",
        "    num_iterations=num_iterations,\n",
        "    centroid_offset=centroid_offset,\n",
        "    rate_instead_number=rate_instead_number,\n",
        "    instance_wise_loss_no_tp=True,\n",
        ")\n",
        "\n",
        "seg_pixel, seg_instance, seg_instance_out, seg_center, seg_fdr, cc_falsed, cc_missed = dici_loss_function(\n",
        "    outputs, # outputs, zeros\n",
        "    labels,  # labels, ones\n",
        ")\n",
        "\n",
        "print(\"dICI Loss\")\n",
        "print(\"seg_pixel (dice) :\", seg_pixel, \" - with gradients? \", seg_pixel.requires_grad)\n",
        "print(\"seg_instance     :\", seg_instance, \" - with gradients? \", seg_instance.requires_grad)\n",
        "print(\"seg_instance_out :\", seg_instance_out, \" - with gradients? \", seg_instance_out.requires_grad)\n",
        "print(\"seg_center       :\", seg_center, \" - with gradients? \", seg_center.requires_grad)\n",
        "print(\"seg_false        :\", seg_fdr, \" - with gradients? \", seg_fdr.requires_grad)\n",
        "print(\"num false        :\", cc_falsed, \" - with gradients? \", cc_falsed.requires_grad)\n",
        "print(\"num missed       :\", cc_missed, \" - with gradients? \", cc_missed.requires_grad)\n",
        "print(\"DONE!\")"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We also can pre-compute the connected components (CC) of manual labels to reduce the computing time. The pre-computed CC image can be passed into the instance of ICILoss and dICILoss classes. Note that in this example, the CC of manual label is computed before the calculation of ICI and dICI losses."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "54geaRmJt2b1",
        "outputId": "a8a8405b-a1a9-424f-e421-9474cefd08b4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "ICI Loss (with pre-computed connected components for labels)\n",
            "seg_pixel (dice) : tensor(0.4587, device='cuda:0', dtype=torch.float64, grad_fn=<MeanBackward0>)  - with gradients?  True\n",
            "seg_instance     : tensor(0.4786, device='cuda:0', dtype=torch.float64, grad_fn=<MeanBackward0>)  - with gradients?  True\n",
            "seg_center       : tensor(0.5793, device='cuda:0', dtype=torch.float64, grad_fn=<MeanBackward0>)  - with gradients?  True\n",
            "seg_false        : tensor(0.5056, device='cuda:0', dtype=torch.float64, grad_fn=<MeanBackward0>)  - with gradients?  True\n",
            "num false        : tensor(2., device='cuda:0', dtype=torch.float64, grad_fn=<MeanBackward0>)  - with gradients?  True\n",
            "num missed       : tensor(1., device='cuda:0', dtype=torch.float64, grad_fn=<MeanBackward0>)  - with gradients?  True\n",
            "DONE!\n",
            "\n",
            "DICILosss (with pre-computed connected components for labels)\n",
            "seg_pixel (dice) : tensor(0.4587, device='cuda:0', dtype=torch.float64, grad_fn=<MeanBackward0>)  - with gradients?  True\n",
            "seg_instance     : tensor(0.4786, device='cuda:0', dtype=torch.float64, grad_fn=<MeanBackward0>) True\n",
            "seg_instance_out : tensor(0.6545, device='cuda:0', dtype=torch.float64, grad_fn=<MeanBackward0>) True\n",
            "seg_center       : tensor(0.5793, device='cuda:0', dtype=torch.float64, grad_fn=<MeanBackward0>) True\n",
            "seg_false        : tensor(0.5056, device='cuda:0', dtype=torch.float64, grad_fn=<MeanBackward0>) True\n",
            "num false        : tensor(2., device='cuda:0', dtype=torch.float64, grad_fn=<MeanBackward0>) True\n",
            "num missed       : tensor(1., device='cuda:0', dtype=torch.float64, grad_fn=<MeanBackward0>) True\n",
            "DONE!\n"
          ]
        }
      ],
      "source": [
        "cc_labels = tools.connected_components(labels, 350, 3)\n",
        "\n",
        "seg_pixel, seg_instance, seg_center, seg_fdr, cc_falsed, cc_missed = ici_loss_function(\n",
        "    outputs, # outputs, zeros\n",
        "    labels,  # labels, ones\n",
        "    cc_labels\n",
        ")\n",
        "\n",
        "print(\"ICI Loss (with pre-computed connected components for labels)\")\n",
        "print(\"seg_pixel (dice) :\", seg_pixel, \" - with gradients? \", seg_pixel.requires_grad)\n",
        "print(\"seg_instance     :\", seg_instance, \" - with gradients? \", seg_instance.requires_grad)\n",
        "print(\"seg_center       :\", seg_center, \" - with gradients? \", seg_center.requires_grad)\n",
        "print(\"seg_false        :\", seg_fdr, \" - with gradients? \", seg_fdr.requires_grad)\n",
        "print(\"num false        :\", cc_falsed, \" - with gradients? \", cc_falsed.requires_grad)\n",
        "print(\"num missed       :\", cc_missed, \" - with gradients? \", cc_missed.requires_grad)\n",
        "print(\"DONE!\")\n",
        "\n",
        "\n",
        "seg_pixel, seg_instance, seg_instance_out, seg_center, seg_fdr, cc_falsed, cc_missed = dici_loss_function(\n",
        "    outputs, # outputs, zeros\n",
        "    labels,  # labels, ones\n",
        "    cc_labels\n",
        ")\n",
        "\n",
        "print(\"\\nDICILosss (with pre-computed connected components for labels)\")\n",
        "print(\"seg_pixel (dice) :\", seg_pixel, \" - with gradients? \", seg_pixel.requires_grad)\n",
        "print(\"seg_instance     :\", seg_instance, seg_instance.requires_grad)\n",
        "print(\"seg_instance_out :\", seg_instance_out, seg_instance_out.requires_grad)\n",
        "print(\"seg_center       :\", seg_center, seg_center.requires_grad)\n",
        "print(\"seg_false        :\", seg_fdr, seg_fdr.requires_grad)\n",
        "print(\"num false        :\", cc_falsed, cc_falsed.requires_grad)\n",
        "print(\"num missed       :\", cc_missed, cc_missed.requires_grad)\n",
        "print(\"DONE!\")"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We also can change any values of ICILoss and dICILoss parameters on the fly by using internal function named `edit_parameters`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ki4kkKDSt2b1",
        "outputId": "53658ffd-b363-450c-8968-b471ebd08ea7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "ICI Loss\n",
            "seg_pixel (dice) : tensor(0.4587, device='cuda:0', dtype=torch.float64, grad_fn=<MeanBackward0>)  - with gradients?  True\n",
            "seg_instance     : tensor(0.4786, device='cuda:0', dtype=torch.float64, grad_fn=<MeanBackward0>)  - with gradients?  True\n",
            "seg_center       : tensor(0.5793, device='cuda:0', dtype=torch.float64, grad_fn=<MeanBackward0>)  - with gradients?  True\n",
            "seg_false        : tensor(0.5056, device='cuda:0', dtype=torch.float64, grad_fn=<MeanBackward0>)  - with gradients?  True\n",
            "num false        : tensor(2., device='cuda:0', dtype=torch.float64, grad_fn=<MeanBackward0>)  - with gradients?  True\n",
            "num missed       : tensor(1., device='cuda:0', dtype=torch.float64, grad_fn=<MeanBackward0>)  - with gradients?  True\n",
            "DONE!\n",
            "\n",
            "ICI Loss (with rates of false and missed instances instead of numbers)\n",
            "seg_pixel (dice) : tensor(0.4587, device='cuda:0', dtype=torch.float64, grad_fn=<MeanBackward0>)  - with gradients?  True\n",
            "seg_instance     : tensor(0.4786, device='cuda:0', dtype=torch.float64, grad_fn=<MeanBackward0>)  - with gradients?  True\n",
            "seg_center       : tensor(0.5793, device='cuda:0', dtype=torch.float64, grad_fn=<MeanBackward0>)  - with gradients?  True\n",
            "seg_false        : tensor(0.5056, device='cuda:0', dtype=torch.float64, grad_fn=<MeanBackward0>)  - with gradients?  True\n",
            "rate false       : tensor(0.2500, device='cuda:0', dtype=torch.float64, grad_fn=<MeanBackward0>)  - with gradients?  True\n",
            "rate missed      : tensor(0.2000, device='cuda:0', dtype=torch.float64, grad_fn=<MeanBackward0>)  - with gradients?  True\n",
            "DONE!\n"
          ]
        }
      ],
      "source": [
        "seg_pixel, seg_instance, seg_center, seg_fdr, cc_falsed, cc_missed = ici_loss_function(\n",
        "    outputs, # outputs, zeros\n",
        "    labels,  # labels, ones\n",
        ")\n",
        "\n",
        "print(\"ICI Loss\")\n",
        "print(\"seg_pixel (dice) :\", seg_pixel, \" - with gradients? \", seg_pixel.requires_grad)\n",
        "print(\"seg_instance     :\", seg_instance, \" - with gradients? \", seg_instance.requires_grad)\n",
        "print(\"seg_center       :\", seg_center, \" - with gradients? \", seg_center.requires_grad)\n",
        "print(\"seg_false        :\", seg_fdr, \" - with gradients? \", seg_fdr.requires_grad)\n",
        "print(\"num false        :\", cc_falsed, \" - with gradients? \", cc_falsed.requires_grad)\n",
        "print(\"num missed       :\", cc_missed, \" - with gradients? \", cc_missed.requires_grad)\n",
        "print(\"DONE!\")\n",
        "\n",
        "ici_loss_function.edit_parameters(rate_instead_number=True)\n",
        "seg_pixel, seg_instance, seg_center, seg_fdr, cc_falsed, cc_missed = ici_loss_function(\n",
        "    outputs, # outputs, zeros\n",
        "    labels,  # labels, ones\n",
        "    cc_labels\n",
        ")\n",
        "\n",
        "print(\"\\nICI Loss (with rates of false and missed instances instead of numbers)\")\n",
        "print(\"seg_pixel (dice) :\", seg_pixel, \" - with gradients? \", seg_pixel.requires_grad)\n",
        "print(\"seg_instance     :\", seg_instance, \" - with gradients? \", seg_instance.requires_grad)\n",
        "print(\"seg_center       :\", seg_center, \" - with gradients? \", seg_center.requires_grad)\n",
        "print(\"seg_false        :\", seg_fdr, \" - with gradients? \", seg_fdr.requires_grad)\n",
        "print(\"rate false       :\", cc_falsed, \" - with gradients? \", cc_falsed.requires_grad)\n",
        "print(\"rate missed      :\", cc_missed, \" - with gradients? \", cc_missed.requires_grad)\n",
        "print(\"DONE!\")"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Full information of ICILoss"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Information of ICI loss can be seen by calling `help(ICILoss)`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0PUmUT_ht2b2",
        "outputId": "645f5b33-2f03-449c-f27e-1015327b186e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Help on class ICILoss in module losses.ICI_loss:\n",
            "\n",
            "class ICILoss(torch.nn.modules.loss._Loss)\n",
            " |  ICILoss(loss_function_pixel, loss_function_instance, loss_function_center, activation='sigmoid', num_out_chn=1, object_chn=1, spatial_dims=3, reduce_segmentation='mean', instance_wise_reduce='instance', num_iterations=350, segmentation_threshold=0.5, max_cc_out=50, mul_too_many=50, min_instance_size=0, centroid_offset=5, smoother=1e-07, instance_wise_loss_no_tp=True, rate_instead_number=False, weighted_instance_wise=False, weighted_fdr=False)\n",
            " |  \n",
            " |  Compute Instance-wise and Center-of-Instance (ICI) segmentation loss between two tensors.\n",
            " |  The data `outputs` (BNHW[D] where N is number of classes) is compared with ground truth `labels` (BNHW[D]).\n",
            " |  \n",
            " |  For a two-class segmentation problem (i.e. background and foreground classes), either sigmoid or softmax\n",
            " |  non-linear function can be called within the function loss (set \"activation = 'sigmoid'\" or \"activation = 'softmax'\",\n",
            " |  respectively). If there are more than two classes, only sigmoid non-linear function can be called. However, user \n",
            " |  can always call any non-linear functions outside the function loss and pass both `outputs` and `labels` tensors\n",
            " |  as probability values (set \"activation == 'none'\"). Note that each channel must be calculated separately\n",
            " |  \n",
            " |  Connected component analysis (CCA) can be called within the loss function for both `outputs` and `labels` tensors.\n",
            " |  However, CCA for `labels` tensor can be pre-computed outside the loss function (pass a tensor which contains the\n",
            " |  CCA's output to the `cc_label_batch`). The \"cc_label_batch = None\" is set to be the default where the CCA for \n",
            " |  `labels` tensor is computed within the loss function.\n",
            " |  \n",
            " |  The CCA is adapted from Kornia library `here <https://kornia-tutorials.readthedocs.io/en/latest/connected_components.html>`\n",
            " |  \n",
            " |  The original paper: TBA\n",
            " |  \n",
            " |  Method resolution order:\n",
            " |      ICILoss\n",
            " |      torch.nn.modules.loss._Loss\n",
            " |      torch.nn.modules.module.Module\n",
            " |      builtins.object\n",
            " |  \n",
            " |  Methods defined here:\n",
            " |  \n",
            " |  __init__(self, loss_function_pixel, loss_function_instance, loss_function_center, activation='sigmoid', num_out_chn=1, object_chn=1, spatial_dims=3, reduce_segmentation='mean', instance_wise_reduce='instance', num_iterations=350, segmentation_threshold=0.5, max_cc_out=50, mul_too_many=50, min_instance_size=0, centroid_offset=5, smoother=1e-07, instance_wise_loss_no_tp=True, rate_instead_number=False, weighted_instance_wise=False, weighted_fdr=False)\n",
            " |      Args:\n",
            " |          loss_function_pixel: Any segmentation loss used to calculate the quality of segmentation in pixel-wise level.\n",
            " |              Written in the original paper as L_{global} in the formalism. \n",
            " |          loss_function_instance: Any segmentation loss used to calculate the quality of segmentation in instance-wise level.\n",
            " |              Written in the original paper as L_{instance} in the formalism.\n",
            " |          loss_function_center: Any segmentation loss used to calculate the center-of-instance segmentation loss.\n",
            " |              Written in the original paper as L_{center} in the formalism.\n",
            " |          activation: Set the non-linear function used for segmentation in the last layer of the model.\n",
            " |              The valid inputs are \"sigmoid\", \"softmax\", or \"none\".\n",
            " |          num_out_chn: Number of channels/classes that `outputs` and `labels` tensors (BNHW[D] where N is number of classes).\n",
            " |          object_chn: Channel of `outputs` and `labels` tensors that this loss function will calculate. Note that each channel\n",
            " |              must be calculated separately.\n",
            " |          spatial_dims: 3 for 3D data (BNHWD) or 2 for 2D data (BNHW).\n",
            " |          reduce_segmentation: Set as `mean` if we want to calculate the average of all instance-wise segmentation losses \n",
            " |              losses or `sum` if we want to calculate the sum of all instance-wise segmentation losses. Default: `mean`.\n",
            " |          instance_wise_reduce: Reducing the instance-wise segmentation losses for all instances or batches. Default: `instance`.\n",
            " |          num_iterations: Number of iterations of max-pooling to perform connected component analysis (CCA). Bigger instances\n",
            " |              need more iterations (or 1 big instance might be devided into several instances). Bigger image also tend to use\n",
            " |              more iterations as well. More iterations will use more computational time. Default: 350\n",
            " |          segmentation_threshold: Segmentation threshold to produce binary predicted segmentation before runnning the CCA.\n",
            " |          max_cc_out: Maximum numbers of connected components in the `outputs` tensor. This is useful to cut down the computation\n",
            " |              time and memory usage in the GPUs. This is extremely useful in the early epochs where there are a lot of false\n",
            " |              predicted segmentation instances. We found that `max_cc_out = 50` produces good performances and time/memory usage.\n",
            " |          mul_too_many: Similar to the 'max_cc_out'.  We found that `mul_too_many = 50` produces good performances and time/memory usage.\n",
            " |          min_instance_size: We can ignore instances that are too small. Set as 0 as the default (i.e. not in use).\n",
            " |          centroid_offset: Offset value to increase the size of center-of-mass for each instance. For example, `centroid_offset = 1` will\n",
            " |              increase the size of center-of-mass of instance in 2D from `1 x 1` into `3 x 3` Default: 3 (i.e. center-of-mass's size is\n",
            " |              either `7 x 7` in 2D or `7 x 7 x 7` in 3D).\n",
            " |          smoother: Used to avoid division by 0 (numerical purposes).\n",
            " |          instance_wise_loss_no_tp: If `True`, the loss function does not include true positive intersections with other instances \n",
            " |              from the ground truth image (please see Appendix B of the original paper). Default: True (mainly due to successfully \n",
            " |              improving the performance in DSC).\n",
            " |          rate_instead_number: The loss function will automatically provides the numbers of both missed and false instances.\n",
            " |              If `False`, the loss function will provide the exact numbers of missed and false instances (e.g. 1 missed and 6 false).\n",
            " |              If `True`, the loss function will provide the rate of missed and false instances (e.g. 1 / 7 = 0.1429 for missed instances\n",
            " |              and 6 / 14 = 0.4286 for false instances).\n",
            " |          weighted_instance_wise: Not currently used and never been tested, so please do not use it. Default to False.\n",
            " |          weighted_fdr: Not currently used and never been tested, so please do not use it. Default to False.\n",
            " |      \n",
            " |      Raises:\n",
            " |          ValueError: When ``activation`` is not one of [\"sigmoid\", \"softmax\", \"none\"].\n",
            " |          ValueError: When ``reduce_segmentation`` is not one of [\"mean\", \"sum\"].\n",
            " |          ValueError: When ``instance_wise_reduce`` is not one of [\"instance\", \"data\"].\n",
            " |  \n",
            " |  edit_parameters(self, loss_function_pixel=None, loss_function_instance=None, loss_function_center=None, num_out_chn=None, object_chn=None, spatial_dims=None, activation=None, reduce_segmentation=None, instance_wise_reduce=None, num_iterations=None, max_cc_out=None, min_instance_size=None, segmentation_threshold=None, centroid_offset=None, mul_too_many=None, smoother=None, instance_wise_loss_no_tp=None, rate_instead_number=None, weighted_instance_wise=None, weighted_fdr=None)\n",
            " |      All parameters can be edited/updated on the fly, so users do not have to create different instance of the loss function.\n",
            " |      Arguments are the same as in the initialization, but the user can choose any parameter that will be edited/updated.\n",
            " |  \n",
            " |  forward(self, outputs, labels, cc_label_batch=None)\n",
            " |      Args:\n",
            " |          outputs: The predicted segmentation. The shape should be BNH[WD], where N is the number of classes.\n",
            " |          labels: The manual label/mask. The shape should be BNH[WD] or B1H[WD], where N is the number of classes.\n",
            " |          cc_label_batch: The pre-computed CCA of the `labels`. If `cc_label_batch = None` the loss function\n",
            " |              will perform CCA to the `labels` tensor.\n",
            " |      \n",
            " |          WARNING: The loss function does not check whether the shapes of `outputs`, `labels`, and `cc_label_batch`\n",
            " |              are the same.\n",
            " |      \n",
            " |      Outputs:\n",
            " |          1. Pixel-wise segmentation loss\n",
            " |          2. Instance-wise segmentation loss\n",
            " |          3. Center-of-instance segmentation loss\n",
            " |          4. Pixel-wise false detection rate (FDR)\n",
            " |          5. Number/rate of false instances\n",
            " |          6. Number/rate of missed instances\n",
            " |      \n",
            " |      Raises:\n",
            " |          ValueError: When ``self.reduction`` is not one of [\"mean\", \"sum\", \"none\"].\n",
            " |      \n",
            " |      Example:\n",
            " |          >>> import torch\n",
            " |          >>> from ICILoss.ICI_loss import ICILoss\n",
            " |          >>> from monai.losses import DiceLoss\n",
            " |          >>> from monai.data.synthetic import create_test_image_3d\n",
            " |          >>> loss_dice = DiceLoss(\n",
            " |          >>>         to_onehot_y=False, \n",
            " |          >>>         sigmoid=False, \n",
            " |          >>>         softmax=False\n",
            " |          >>>     )\n",
            " |          >>> _, lbl = create_test_image_3d(128, 128, 128, num_seg_classes=1)\n",
            " |          >>> _, out = create_test_image_3d(128, 128, 128, num_seg_classes=1)\n",
            " |          >>> lbl = torch.reshape(torch.tensor(lbl).float(), (1, 1, 128, 128, 128))\n",
            " |          >>> out = torch.reshape(torch.tensor(out).float(), (1, 1, 128, 128, 128))\n",
            " |          >>> ici_loss_function = ICILoss(\n",
            " |          >>>         loss_function_pixel=loss_dice,\n",
            " |          >>>         loss_function_instance=loss_dice,\n",
            " |          >>>         loss_function_center=loss_dice,\n",
            " |          >>>         num_out_chn = 1,\n",
            " |          >>>         activation=\"none\",\n",
            " |          >>>     )\n",
            " |          >>> ici_loss_function.print_parameters()\n",
            " |          >>> seg_pixel, seg_instance, seg_center, fdr, cc_falsed, cc_missed = ici_loss_function(\n",
            " |          >>>         out,\n",
            " |          >>>         lbl\n",
            " |          >>>     )\n",
            " |  \n",
            " |  print_parameters(self)\n",
            " |      Print all parameters currently being used by the loss function.\n",
            " |  \n",
            " |  ----------------------------------------------------------------------\n",
            " |  Data and other attributes inherited from torch.nn.modules.loss._Loss:\n",
            " |  \n",
            " |  __annotations__ = {'reduction': <class 'str'>}\n",
            " |  \n",
            " |  ----------------------------------------------------------------------\n",
            " |  Methods inherited from torch.nn.modules.module.Module:\n",
            " |  \n",
            " |  __call__ = _call_impl(self, *args, **kwargs)\n",
            " |  \n",
            " |  __delattr__(self, name)\n",
            " |      Implement delattr(self, name).\n",
            " |  \n",
            " |  __dir__(self)\n",
            " |      Default dir() implementation.\n",
            " |  \n",
            " |  __getattr__(self, name: str) -> Union[torch.Tensor, ForwardRef('Module')]\n",
            " |  \n",
            " |  __repr__(self)\n",
            " |      Return repr(self).\n",
            " |  \n",
            " |  __setattr__(self, name: str, value: Union[torch.Tensor, ForwardRef('Module')]) -> None\n",
            " |      Implement setattr(self, name, value).\n",
            " |  \n",
            " |  __setstate__(self, state)\n",
            " |  \n",
            " |  add_module(self, name: str, module: Optional[ForwardRef('Module')]) -> None\n",
            " |      Adds a child module to the current module.\n",
            " |      \n",
            " |      The module can be accessed as an attribute using the given name.\n",
            " |      \n",
            " |      Args:\n",
            " |          name (str): name of the child module. The child module can be\n",
            " |              accessed from this module using the given name\n",
            " |          module (Module): child module to be added to the module.\n",
            " |  \n",
            " |  apply(self: ~T, fn: Callable[[ForwardRef('Module')], NoneType]) -> ~T\n",
            " |      Applies ``fn`` recursively to every submodule (as returned by ``.children()``)\n",
            " |      as well as self. Typical use includes initializing the parameters of a model\n",
            " |      (see also :ref:`nn-init-doc`).\n",
            " |      \n",
            " |      Args:\n",
            " |          fn (:class:`Module` -> None): function to be applied to each submodule\n",
            " |      \n",
            " |      Returns:\n",
            " |          Module: self\n",
            " |      \n",
            " |      Example::\n",
            " |      \n",
            " |          >>> @torch.no_grad()\n",
            " |          >>> def init_weights(m):\n",
            " |          >>>     print(m)\n",
            " |          >>>     if type(m) == nn.Linear:\n",
            " |          >>>         m.weight.fill_(1.0)\n",
            " |          >>>         print(m.weight)\n",
            " |          >>> net = nn.Sequential(nn.Linear(2, 2), nn.Linear(2, 2))\n",
            " |          >>> net.apply(init_weights)\n",
            " |          Linear(in_features=2, out_features=2, bias=True)\n",
            " |          Parameter containing:\n",
            " |          tensor([[1., 1.],\n",
            " |                  [1., 1.]], requires_grad=True)\n",
            " |          Linear(in_features=2, out_features=2, bias=True)\n",
            " |          Parameter containing:\n",
            " |          tensor([[1., 1.],\n",
            " |                  [1., 1.]], requires_grad=True)\n",
            " |          Sequential(\n",
            " |            (0): Linear(in_features=2, out_features=2, bias=True)\n",
            " |            (1): Linear(in_features=2, out_features=2, bias=True)\n",
            " |          )\n",
            " |  \n",
            " |  bfloat16(self: ~T) -> ~T\n",
            " |      Casts all floating point parameters and buffers to ``bfloat16`` datatype.\n",
            " |      \n",
            " |      .. note::\n",
            " |          This method modifies the module in-place.\n",
            " |      \n",
            " |      Returns:\n",
            " |          Module: self\n",
            " |  \n",
            " |  buffers(self, recurse: bool = True) -> Iterator[torch.Tensor]\n",
            " |      Returns an iterator over module buffers.\n",
            " |      \n",
            " |      Args:\n",
            " |          recurse (bool): if True, then yields buffers of this module\n",
            " |              and all submodules. Otherwise, yields only buffers that\n",
            " |              are direct members of this module.\n",
            " |      \n",
            " |      Yields:\n",
            " |          torch.Tensor: module buffer\n",
            " |      \n",
            " |      Example::\n",
            " |      \n",
            " |          >>> # xdoctest: +SKIP(\"undefined vars\")\n",
            " |          >>> for buf in model.buffers():\n",
            " |          >>>     print(type(buf), buf.size())\n",
            " |          <class 'torch.Tensor'> (20L,)\n",
            " |          <class 'torch.Tensor'> (20L, 1L, 5L, 5L)\n",
            " |  \n",
            " |  children(self) -> Iterator[ForwardRef('Module')]\n",
            " |      Returns an iterator over immediate children modules.\n",
            " |      \n",
            " |      Yields:\n",
            " |          Module: a child module\n",
            " |  \n",
            " |  cpu(self: ~T) -> ~T\n",
            " |      Moves all model parameters and buffers to the CPU.\n",
            " |      \n",
            " |      .. note::\n",
            " |          This method modifies the module in-place.\n",
            " |      \n",
            " |      Returns:\n",
            " |          Module: self\n",
            " |  \n",
            " |  cuda(self: ~T, device: Union[int, torch.device, NoneType] = None) -> ~T\n",
            " |      Moves all model parameters and buffers to the GPU.\n",
            " |      \n",
            " |      This also makes associated parameters and buffers different objects. So\n",
            " |      it should be called before constructing optimizer if the module will\n",
            " |      live on GPU while being optimized.\n",
            " |      \n",
            " |      .. note::\n",
            " |          This method modifies the module in-place.\n",
            " |      \n",
            " |      Args:\n",
            " |          device (int, optional): if specified, all parameters will be\n",
            " |              copied to that device\n",
            " |      \n",
            " |      Returns:\n",
            " |          Module: self\n",
            " |  \n",
            " |  double(self: ~T) -> ~T\n",
            " |      Casts all floating point parameters and buffers to ``double`` datatype.\n",
            " |      \n",
            " |      .. note::\n",
            " |          This method modifies the module in-place.\n",
            " |      \n",
            " |      Returns:\n",
            " |          Module: self\n",
            " |  \n",
            " |  eval(self: ~T) -> ~T\n",
            " |      Sets the module in evaluation mode.\n",
            " |      \n",
            " |      This has any effect only on certain modules. See documentations of\n",
            " |      particular modules for details of their behaviors in training/evaluation\n",
            " |      mode, if they are affected, e.g. :class:`Dropout`, :class:`BatchNorm`,\n",
            " |      etc.\n",
            " |      \n",
            " |      This is equivalent with :meth:`self.train(False) <torch.nn.Module.train>`.\n",
            " |      \n",
            " |      See :ref:`locally-disable-grad-doc` for a comparison between\n",
            " |      `.eval()` and several similar mechanisms that may be confused with it.\n",
            " |      \n",
            " |      Returns:\n",
            " |          Module: self\n",
            " |  \n",
            " |  extra_repr(self) -> str\n",
            " |      Set the extra representation of the module\n",
            " |      \n",
            " |      To print customized extra information, you should re-implement\n",
            " |      this method in your own modules. Both single-line and multi-line\n",
            " |      strings are acceptable.\n",
            " |  \n",
            " |  float(self: ~T) -> ~T\n",
            " |      Casts all floating point parameters and buffers to ``float`` datatype.\n",
            " |      \n",
            " |      .. note::\n",
            " |          This method modifies the module in-place.\n",
            " |      \n",
            " |      Returns:\n",
            " |          Module: self\n",
            " |  \n",
            " |  get_buffer(self, target: str) -> 'Tensor'\n",
            " |      Returns the buffer given by ``target`` if it exists,\n",
            " |      otherwise throws an error.\n",
            " |      \n",
            " |      See the docstring for ``get_submodule`` for a more detailed\n",
            " |      explanation of this method's functionality as well as how to\n",
            " |      correctly specify ``target``.\n",
            " |      \n",
            " |      Args:\n",
            " |          target: The fully-qualified string name of the buffer\n",
            " |              to look for. (See ``get_submodule`` for how to specify a\n",
            " |              fully-qualified string.)\n",
            " |      \n",
            " |      Returns:\n",
            " |          torch.Tensor: The buffer referenced by ``target``\n",
            " |      \n",
            " |      Raises:\n",
            " |          AttributeError: If the target string references an invalid\n",
            " |              path or resolves to something that is not a\n",
            " |              buffer\n",
            " |  \n",
            " |  get_extra_state(self) -> Any\n",
            " |      Returns any extra state to include in the module's state_dict.\n",
            " |      Implement this and a corresponding :func:`set_extra_state` for your module\n",
            " |      if you need to store extra state. This function is called when building the\n",
            " |      module's `state_dict()`.\n",
            " |      \n",
            " |      Note that extra state should be picklable to ensure working serialization\n",
            " |      of the state_dict. We only provide provide backwards compatibility guarantees\n",
            " |      for serializing Tensors; other objects may break backwards compatibility if\n",
            " |      their serialized pickled form changes.\n",
            " |      \n",
            " |      Returns:\n",
            " |          object: Any extra state to store in the module's state_dict\n",
            " |  \n",
            " |  get_parameter(self, target: str) -> 'Parameter'\n",
            " |      Returns the parameter given by ``target`` if it exists,\n",
            " |      otherwise throws an error.\n",
            " |      \n",
            " |      See the docstring for ``get_submodule`` for a more detailed\n",
            " |      explanation of this method's functionality as well as how to\n",
            " |      correctly specify ``target``.\n",
            " |      \n",
            " |      Args:\n",
            " |          target: The fully-qualified string name of the Parameter\n",
            " |              to look for. (See ``get_submodule`` for how to specify a\n",
            " |              fully-qualified string.)\n",
            " |      \n",
            " |      Returns:\n",
            " |          torch.nn.Parameter: The Parameter referenced by ``target``\n",
            " |      \n",
            " |      Raises:\n",
            " |          AttributeError: If the target string references an invalid\n",
            " |              path or resolves to something that is not an\n",
            " |              ``nn.Parameter``\n",
            " |  \n",
            " |  get_submodule(self, target: str) -> 'Module'\n",
            " |      Returns the submodule given by ``target`` if it exists,\n",
            " |      otherwise throws an error.\n",
            " |      \n",
            " |      For example, let's say you have an ``nn.Module`` ``A`` that\n",
            " |      looks like this:\n",
            " |      \n",
            " |      .. code-block:: text\n",
            " |      \n",
            " |          A(\n",
            " |              (net_b): Module(\n",
            " |                  (net_c): Module(\n",
            " |                      (conv): Conv2d(16, 33, kernel_size=(3, 3), stride=(2, 2))\n",
            " |                  )\n",
            " |                  (linear): Linear(in_features=100, out_features=200, bias=True)\n",
            " |              )\n",
            " |          )\n",
            " |      \n",
            " |      (The diagram shows an ``nn.Module`` ``A``. ``A`` has a nested\n",
            " |      submodule ``net_b``, which itself has two submodules ``net_c``\n",
            " |      and ``linear``. ``net_c`` then has a submodule ``conv``.)\n",
            " |      \n",
            " |      To check whether or not we have the ``linear`` submodule, we\n",
            " |      would call ``get_submodule(\"net_b.linear\")``. To check whether\n",
            " |      we have the ``conv`` submodule, we would call\n",
            " |      ``get_submodule(\"net_b.net_c.conv\")``.\n",
            " |      \n",
            " |      The runtime of ``get_submodule`` is bounded by the degree\n",
            " |      of module nesting in ``target``. A query against\n",
            " |      ``named_modules`` achieves the same result, but it is O(N) in\n",
            " |      the number of transitive modules. So, for a simple check to see\n",
            " |      if some submodule exists, ``get_submodule`` should always be\n",
            " |      used.\n",
            " |      \n",
            " |      Args:\n",
            " |          target: The fully-qualified string name of the submodule\n",
            " |              to look for. (See above example for how to specify a\n",
            " |              fully-qualified string.)\n",
            " |      \n",
            " |      Returns:\n",
            " |          torch.nn.Module: The submodule referenced by ``target``\n",
            " |      \n",
            " |      Raises:\n",
            " |          AttributeError: If the target string references an invalid\n",
            " |              path or resolves to something that is not an\n",
            " |              ``nn.Module``\n",
            " |  \n",
            " |  half(self: ~T) -> ~T\n",
            " |      Casts all floating point parameters and buffers to ``half`` datatype.\n",
            " |      \n",
            " |      .. note::\n",
            " |          This method modifies the module in-place.\n",
            " |      \n",
            " |      Returns:\n",
            " |          Module: self\n",
            " |  \n",
            " |  ipu(self: ~T, device: Union[int, torch.device, NoneType] = None) -> ~T\n",
            " |      Moves all model parameters and buffers to the IPU.\n",
            " |      \n",
            " |      This also makes associated parameters and buffers different objects. So\n",
            " |      it should be called before constructing optimizer if the module will\n",
            " |      live on IPU while being optimized.\n",
            " |      \n",
            " |      .. note::\n",
            " |          This method modifies the module in-place.\n",
            " |      \n",
            " |      Arguments:\n",
            " |          device (int, optional): if specified, all parameters will be\n",
            " |              copied to that device\n",
            " |      \n",
            " |      Returns:\n",
            " |          Module: self\n",
            " |  \n",
            " |  load_state_dict(self, state_dict: Mapping[str, Any], strict: bool = True)\n",
            " |      Copies parameters and buffers from :attr:`state_dict` into\n",
            " |      this module and its descendants. If :attr:`strict` is ``True``, then\n",
            " |      the keys of :attr:`state_dict` must exactly match the keys returned\n",
            " |      by this module's :meth:`~torch.nn.Module.state_dict` function.\n",
            " |      \n",
            " |      Args:\n",
            " |          state_dict (dict): a dict containing parameters and\n",
            " |              persistent buffers.\n",
            " |          strict (bool, optional): whether to strictly enforce that the keys\n",
            " |              in :attr:`state_dict` match the keys returned by this module's\n",
            " |              :meth:`~torch.nn.Module.state_dict` function. Default: ``True``\n",
            " |      \n",
            " |      Returns:\n",
            " |          ``NamedTuple`` with ``missing_keys`` and ``unexpected_keys`` fields:\n",
            " |              * **missing_keys** is a list of str containing the missing keys\n",
            " |              * **unexpected_keys** is a list of str containing the unexpected keys\n",
            " |      \n",
            " |      Note:\n",
            " |          If a parameter or buffer is registered as ``None`` and its corresponding key\n",
            " |          exists in :attr:`state_dict`, :meth:`load_state_dict` will raise a\n",
            " |          ``RuntimeError``.\n",
            " |  \n",
            " |  modules(self) -> Iterator[ForwardRef('Module')]\n",
            " |      Returns an iterator over all modules in the network.\n",
            " |      \n",
            " |      Yields:\n",
            " |          Module: a module in the network\n",
            " |      \n",
            " |      Note:\n",
            " |          Duplicate modules are returned only once. In the following\n",
            " |          example, ``l`` will be returned only once.\n",
            " |      \n",
            " |      Example::\n",
            " |      \n",
            " |          >>> l = nn.Linear(2, 2)\n",
            " |          >>> net = nn.Sequential(l, l)\n",
            " |          >>> for idx, m in enumerate(net.modules()):\n",
            " |          ...     print(idx, '->', m)\n",
            " |      \n",
            " |          0 -> Sequential(\n",
            " |            (0): Linear(in_features=2, out_features=2, bias=True)\n",
            " |            (1): Linear(in_features=2, out_features=2, bias=True)\n",
            " |          )\n",
            " |          1 -> Linear(in_features=2, out_features=2, bias=True)\n",
            " |  \n",
            " |  named_buffers(self, prefix: str = '', recurse: bool = True, remove_duplicate: bool = True) -> Iterator[Tuple[str, torch.Tensor]]\n",
            " |      Returns an iterator over module buffers, yielding both the\n",
            " |      name of the buffer as well as the buffer itself.\n",
            " |      \n",
            " |      Args:\n",
            " |          prefix (str): prefix to prepend to all buffer names.\n",
            " |          recurse (bool, optional): if True, then yields buffers of this module\n",
            " |              and all submodules. Otherwise, yields only buffers that\n",
            " |              are direct members of this module. Defaults to True.\n",
            " |          remove_duplicate (bool, optional): whether to remove the duplicated buffers in the result. Defaults to True.\n",
            " |      \n",
            " |      Yields:\n",
            " |          (str, torch.Tensor): Tuple containing the name and buffer\n",
            " |      \n",
            " |      Example::\n",
            " |      \n",
            " |          >>> # xdoctest: +SKIP(\"undefined vars\")\n",
            " |          >>> for name, buf in self.named_buffers():\n",
            " |          >>>     if name in ['running_var']:\n",
            " |          >>>         print(buf.size())\n",
            " |  \n",
            " |  named_children(self) -> Iterator[Tuple[str, ForwardRef('Module')]]\n",
            " |      Returns an iterator over immediate children modules, yielding both\n",
            " |      the name of the module as well as the module itself.\n",
            " |      \n",
            " |      Yields:\n",
            " |          (str, Module): Tuple containing a name and child module\n",
            " |      \n",
            " |      Example::\n",
            " |      \n",
            " |          >>> # xdoctest: +SKIP(\"undefined vars\")\n",
            " |          >>> for name, module in model.named_children():\n",
            " |          >>>     if name in ['conv4', 'conv5']:\n",
            " |          >>>         print(module)\n",
            " |  \n",
            " |  named_modules(self, memo: Optional[Set[ForwardRef('Module')]] = None, prefix: str = '', remove_duplicate: bool = True)\n",
            " |      Returns an iterator over all modules in the network, yielding\n",
            " |      both the name of the module as well as the module itself.\n",
            " |      \n",
            " |      Args:\n",
            " |          memo: a memo to store the set of modules already added to the result\n",
            " |          prefix: a prefix that will be added to the name of the module\n",
            " |          remove_duplicate: whether to remove the duplicated module instances in the result\n",
            " |              or not\n",
            " |      \n",
            " |      Yields:\n",
            " |          (str, Module): Tuple of name and module\n",
            " |      \n",
            " |      Note:\n",
            " |          Duplicate modules are returned only once. In the following\n",
            " |          example, ``l`` will be returned only once.\n",
            " |      \n",
            " |      Example::\n",
            " |      \n",
            " |          >>> l = nn.Linear(2, 2)\n",
            " |          >>> net = nn.Sequential(l, l)\n",
            " |          >>> for idx, m in enumerate(net.named_modules()):\n",
            " |          ...     print(idx, '->', m)\n",
            " |      \n",
            " |          0 -> ('', Sequential(\n",
            " |            (0): Linear(in_features=2, out_features=2, bias=True)\n",
            " |            (1): Linear(in_features=2, out_features=2, bias=True)\n",
            " |          ))\n",
            " |          1 -> ('0', Linear(in_features=2, out_features=2, bias=True))\n",
            " |  \n",
            " |  named_parameters(self, prefix: str = '', recurse: bool = True, remove_duplicate: bool = True) -> Iterator[Tuple[str, torch.nn.parameter.Parameter]]\n",
            " |      Returns an iterator over module parameters, yielding both the\n",
            " |      name of the parameter as well as the parameter itself.\n",
            " |      \n",
            " |      Args:\n",
            " |          prefix (str): prefix to prepend to all parameter names.\n",
            " |          recurse (bool): if True, then yields parameters of this module\n",
            " |              and all submodules. Otherwise, yields only parameters that\n",
            " |              are direct members of this module.\n",
            " |          remove_duplicate (bool, optional): whether to remove the duplicated\n",
            " |              parameters in the result. Defaults to True.\n",
            " |      \n",
            " |      Yields:\n",
            " |          (str, Parameter): Tuple containing the name and parameter\n",
            " |      \n",
            " |      Example::\n",
            " |      \n",
            " |          >>> # xdoctest: +SKIP(\"undefined vars\")\n",
            " |          >>> for name, param in self.named_parameters():\n",
            " |          >>>     if name in ['bias']:\n",
            " |          >>>         print(param.size())\n",
            " |  \n",
            " |  parameters(self, recurse: bool = True) -> Iterator[torch.nn.parameter.Parameter]\n",
            " |      Returns an iterator over module parameters.\n",
            " |      \n",
            " |      This is typically passed to an optimizer.\n",
            " |      \n",
            " |      Args:\n",
            " |          recurse (bool): if True, then yields parameters of this module\n",
            " |              and all submodules. Otherwise, yields only parameters that\n",
            " |              are direct members of this module.\n",
            " |      \n",
            " |      Yields:\n",
            " |          Parameter: module parameter\n",
            " |      \n",
            " |      Example::\n",
            " |      \n",
            " |          >>> # xdoctest: +SKIP(\"undefined vars\")\n",
            " |          >>> for param in model.parameters():\n",
            " |          >>>     print(type(param), param.size())\n",
            " |          <class 'torch.Tensor'> (20L,)\n",
            " |          <class 'torch.Tensor'> (20L, 1L, 5L, 5L)\n",
            " |  \n",
            " |  register_backward_hook(self, hook: Callable[[ForwardRef('Module'), Union[Tuple[torch.Tensor, ...], torch.Tensor], Union[Tuple[torch.Tensor, ...], torch.Tensor]], Union[NoneType, Tuple[torch.Tensor, ...], torch.Tensor]]) -> torch.utils.hooks.RemovableHandle\n",
            " |      Registers a backward hook on the module.\n",
            " |      \n",
            " |      This function is deprecated in favor of :meth:`~torch.nn.Module.register_full_backward_hook` and\n",
            " |      the behavior of this function will change in future versions.\n",
            " |      \n",
            " |      Returns:\n",
            " |          :class:`torch.utils.hooks.RemovableHandle`:\n",
            " |              a handle that can be used to remove the added hook by calling\n",
            " |              ``handle.remove()``\n",
            " |  \n",
            " |  register_buffer(self, name: str, tensor: Optional[torch.Tensor], persistent: bool = True) -> None\n",
            " |      Adds a buffer to the module.\n",
            " |      \n",
            " |      This is typically used to register a buffer that should not to be\n",
            " |      considered a model parameter. For example, BatchNorm's ``running_mean``\n",
            " |      is not a parameter, but is part of the module's state. Buffers, by\n",
            " |      default, are persistent and will be saved alongside parameters. This\n",
            " |      behavior can be changed by setting :attr:`persistent` to ``False``. The\n",
            " |      only difference between a persistent buffer and a non-persistent buffer\n",
            " |      is that the latter will not be a part of this module's\n",
            " |      :attr:`state_dict`.\n",
            " |      \n",
            " |      Buffers can be accessed as attributes using given names.\n",
            " |      \n",
            " |      Args:\n",
            " |          name (str): name of the buffer. The buffer can be accessed\n",
            " |              from this module using the given name\n",
            " |          tensor (Tensor or None): buffer to be registered. If ``None``, then operations\n",
            " |              that run on buffers, such as :attr:`cuda`, are ignored. If ``None``,\n",
            " |              the buffer is **not** included in the module's :attr:`state_dict`.\n",
            " |          persistent (bool): whether the buffer is part of this module's\n",
            " |              :attr:`state_dict`.\n",
            " |      \n",
            " |      Example::\n",
            " |      \n",
            " |          >>> # xdoctest: +SKIP(\"undefined vars\")\n",
            " |          >>> self.register_buffer('running_mean', torch.zeros(num_features))\n",
            " |  \n",
            " |  register_forward_hook(self, hook: Union[Callable[[~T, Tuple[Any, ...], Any], Optional[Any]], Callable[[~T, Tuple[Any, ...], Dict[str, Any], Any], Optional[Any]]], *, prepend: bool = False, with_kwargs: bool = False) -> torch.utils.hooks.RemovableHandle\n",
            " |      Registers a forward hook on the module.\n",
            " |      \n",
            " |      The hook will be called every time after :func:`forward` has computed an output.\n",
            " |      \n",
            " |      If ``with_kwargs`` is ``False`` or not specified, the input contains only\n",
            " |      the positional arguments given to the module. Keyword arguments won't be\n",
            " |      passed to the hooks and only to the ``forward``. The hook can modify the\n",
            " |      output. It can modify the input inplace but it will not have effect on\n",
            " |      forward since this is called after :func:`forward` is called. The hook\n",
            " |      should have the following signature::\n",
            " |      \n",
            " |          hook(module, args, output) -> None or modified output\n",
            " |      \n",
            " |      If ``with_kwargs`` is ``True``, the forward hook will be passed the\n",
            " |      ``kwargs`` given to the forward function and be expected to return the\n",
            " |      output possibly modified. The hook should have the following signature::\n",
            " |      \n",
            " |          hook(module, args, kwargs, output) -> None or modified output\n",
            " |      \n",
            " |      Args:\n",
            " |          hook (Callable): The user defined hook to be registered.\n",
            " |          prepend (bool): If ``True``, the provided ``hook`` will be fired\n",
            " |              before all existing ``forward`` hooks on this\n",
            " |              :class:`torch.nn.modules.Module`. Otherwise, the provided\n",
            " |              ``hook`` will be fired after all existing ``forward`` hooks on\n",
            " |              this :class:`torch.nn.modules.Module`. Note that global\n",
            " |              ``forward`` hooks registered with\n",
            " |              :func:`register_module_forward_hook` will fire before all hooks\n",
            " |              registered by this method.\n",
            " |              Default: ``False``\n",
            " |          with_kwargs (bool): If ``True``, the ``hook`` will be passed the\n",
            " |              kwargs given to the forward function.\n",
            " |              Default: ``False``\n",
            " |      \n",
            " |      Returns:\n",
            " |          :class:`torch.utils.hooks.RemovableHandle`:\n",
            " |              a handle that can be used to remove the added hook by calling\n",
            " |              ``handle.remove()``\n",
            " |  \n",
            " |  register_forward_pre_hook(self, hook: Union[Callable[[~T, Tuple[Any, ...]], Optional[Any]], Callable[[~T, Tuple[Any, ...], Dict[str, Any]], Optional[Tuple[Any, Dict[str, Any]]]]], *, prepend: bool = False, with_kwargs: bool = False) -> torch.utils.hooks.RemovableHandle\n",
            " |      Registers a forward pre-hook on the module.\n",
            " |      \n",
            " |      The hook will be called every time before :func:`forward` is invoked.\n",
            " |      \n",
            " |      \n",
            " |      If ``with_kwargs`` is false or not specified, the input contains only\n",
            " |      the positional arguments given to the module. Keyword arguments won't be\n",
            " |      passed to the hooks and only to the ``forward``. The hook can modify the\n",
            " |      input. User can either return a tuple or a single modified value in the\n",
            " |      hook. We will wrap the value into a tuple if a single value is returned\n",
            " |      (unless that value is already a tuple). The hook should have the\n",
            " |      following signature::\n",
            " |      \n",
            " |          hook(module, args) -> None or modified input\n",
            " |      \n",
            " |      If ``with_kwargs`` is true, the forward pre-hook will be passed the\n",
            " |      kwargs given to the forward function. And if the hook modifies the\n",
            " |      input, both the args and kwargs should be returned. The hook should have\n",
            " |      the following signature::\n",
            " |      \n",
            " |          hook(module, args, kwargs) -> None or a tuple of modified input and kwargs\n",
            " |      \n",
            " |      Args:\n",
            " |          hook (Callable): The user defined hook to be registered.\n",
            " |          prepend (bool): If true, the provided ``hook`` will be fired before\n",
            " |              all existing ``forward_pre`` hooks on this\n",
            " |              :class:`torch.nn.modules.Module`. Otherwise, the provided\n",
            " |              ``hook`` will be fired after all existing ``forward_pre`` hooks\n",
            " |              on this :class:`torch.nn.modules.Module`. Note that global\n",
            " |              ``forward_pre`` hooks registered with\n",
            " |              :func:`register_module_forward_pre_hook` will fire before all\n",
            " |              hooks registered by this method.\n",
            " |              Default: ``False``\n",
            " |          with_kwargs (bool): If true, the ``hook`` will be passed the kwargs\n",
            " |              given to the forward function.\n",
            " |              Default: ``False``\n",
            " |      \n",
            " |      Returns:\n",
            " |          :class:`torch.utils.hooks.RemovableHandle`:\n",
            " |              a handle that can be used to remove the added hook by calling\n",
            " |              ``handle.remove()``\n",
            " |  \n",
            " |  register_full_backward_hook(self, hook: Callable[[ForwardRef('Module'), Union[Tuple[torch.Tensor, ...], torch.Tensor], Union[Tuple[torch.Tensor, ...], torch.Tensor]], Union[NoneType, Tuple[torch.Tensor, ...], torch.Tensor]], prepend: bool = False) -> torch.utils.hooks.RemovableHandle\n",
            " |      Registers a backward hook on the module.\n",
            " |      \n",
            " |      The hook will be called every time the gradients with respect to a module\n",
            " |      are computed, i.e. the hook will execute if and only if the gradients with\n",
            " |      respect to module outputs are computed. The hook should have the following\n",
            " |      signature::\n",
            " |      \n",
            " |          hook(module, grad_input, grad_output) -> tuple(Tensor) or None\n",
            " |      \n",
            " |      The :attr:`grad_input` and :attr:`grad_output` are tuples that contain the gradients\n",
            " |      with respect to the inputs and outputs respectively. The hook should\n",
            " |      not modify its arguments, but it can optionally return a new gradient with\n",
            " |      respect to the input that will be used in place of :attr:`grad_input` in\n",
            " |      subsequent computations. :attr:`grad_input` will only correspond to the inputs given\n",
            " |      as positional arguments and all kwarg arguments are ignored. Entries\n",
            " |      in :attr:`grad_input` and :attr:`grad_output` will be ``None`` for all non-Tensor\n",
            " |      arguments.\n",
            " |      \n",
            " |      For technical reasons, when this hook is applied to a Module, its forward function will\n",
            " |      receive a view of each Tensor passed to the Module. Similarly the caller will receive a view\n",
            " |      of each Tensor returned by the Module's forward function.\n",
            " |      \n",
            " |      .. warning ::\n",
            " |          Modifying inputs or outputs inplace is not allowed when using backward hooks and\n",
            " |          will raise an error.\n",
            " |      \n",
            " |      Args:\n",
            " |          hook (Callable): The user-defined hook to be registered.\n",
            " |          prepend (bool): If true, the provided ``hook`` will be fired before\n",
            " |              all existing ``backward`` hooks on this\n",
            " |              :class:`torch.nn.modules.Module`. Otherwise, the provided\n",
            " |              ``hook`` will be fired after all existing ``backward`` hooks on\n",
            " |              this :class:`torch.nn.modules.Module`. Note that global\n",
            " |              ``backward`` hooks registered with\n",
            " |              :func:`register_module_full_backward_hook` will fire before\n",
            " |              all hooks registered by this method.\n",
            " |      \n",
            " |      Returns:\n",
            " |          :class:`torch.utils.hooks.RemovableHandle`:\n",
            " |              a handle that can be used to remove the added hook by calling\n",
            " |              ``handle.remove()``\n",
            " |  \n",
            " |  register_full_backward_pre_hook(self, hook: Callable[[ForwardRef('Module'), Union[Tuple[torch.Tensor, ...], torch.Tensor]], Union[NoneType, Tuple[torch.Tensor, ...], torch.Tensor]], prepend: bool = False) -> torch.utils.hooks.RemovableHandle\n",
            " |      Registers a backward pre-hook on the module.\n",
            " |      \n",
            " |      The hook will be called every time the gradients for the module are computed.\n",
            " |      The hook should have the following signature::\n",
            " |      \n",
            " |          hook(module, grad_output) -> Tensor or None\n",
            " |      \n",
            " |      The :attr:`grad_output` is a tuple. The hook should\n",
            " |      not modify its arguments, but it can optionally return a new gradient with\n",
            " |      respect to the output that will be used in place of :attr:`grad_output` in\n",
            " |      subsequent computations. Entries in :attr:`grad_output` will be ``None`` for\n",
            " |      all non-Tensor arguments.\n",
            " |      \n",
            " |      For technical reasons, when this hook is applied to a Module, its forward function will\n",
            " |      receive a view of each Tensor passed to the Module. Similarly the caller will receive a view\n",
            " |      of each Tensor returned by the Module's forward function.\n",
            " |      \n",
            " |      .. warning ::\n",
            " |          Modifying inputs inplace is not allowed when using backward hooks and\n",
            " |          will raise an error.\n",
            " |      \n",
            " |      Args:\n",
            " |          hook (Callable): The user-defined hook to be registered.\n",
            " |          prepend (bool): If true, the provided ``hook`` will be fired before\n",
            " |              all existing ``backward_pre`` hooks on this\n",
            " |              :class:`torch.nn.modules.Module`. Otherwise, the provided\n",
            " |              ``hook`` will be fired after all existing ``backward_pre`` hooks\n",
            " |              on this :class:`torch.nn.modules.Module`. Note that global\n",
            " |              ``backward_pre`` hooks registered with\n",
            " |              :func:`register_module_full_backward_pre_hook` will fire before\n",
            " |              all hooks registered by this method.\n",
            " |      \n",
            " |      Returns:\n",
            " |          :class:`torch.utils.hooks.RemovableHandle`:\n",
            " |              a handle that can be used to remove the added hook by calling\n",
            " |              ``handle.remove()``\n",
            " |  \n",
            " |  register_load_state_dict_post_hook(self, hook)\n",
            " |      Registers a post hook to be run after module's ``load_state_dict``\n",
            " |      is called.\n",
            " |      \n",
            " |      It should have the following signature::\n",
            " |          hook(module, incompatible_keys) -> None\n",
            " |      \n",
            " |      The ``module`` argument is the current module that this hook is registered\n",
            " |      on, and the ``incompatible_keys`` argument is a ``NamedTuple`` consisting\n",
            " |      of attributes ``missing_keys`` and ``unexpected_keys``. ``missing_keys``\n",
            " |      is a ``list`` of ``str`` containing the missing keys and\n",
            " |      ``unexpected_keys`` is a ``list`` of ``str`` containing the unexpected keys.\n",
            " |      \n",
            " |      The given incompatible_keys can be modified inplace if needed.\n",
            " |      \n",
            " |      Note that the checks performed when calling :func:`load_state_dict` with\n",
            " |      ``strict=True`` are affected by modifications the hook makes to\n",
            " |      ``missing_keys`` or ``unexpected_keys``, as expected. Additions to either\n",
            " |      set of keys will result in an error being thrown when ``strict=True``, and\n",
            " |      clearing out both missing and unexpected keys will avoid an error.\n",
            " |      \n",
            " |      Returns:\n",
            " |          :class:`torch.utils.hooks.RemovableHandle`:\n",
            " |              a handle that can be used to remove the added hook by calling\n",
            " |              ``handle.remove()``\n",
            " |  \n",
            " |  register_module(self, name: str, module: Optional[ForwardRef('Module')]) -> None\n",
            " |      Alias for :func:`add_module`.\n",
            " |  \n",
            " |  register_parameter(self, name: str, param: Optional[torch.nn.parameter.Parameter]) -> None\n",
            " |      Adds a parameter to the module.\n",
            " |      \n",
            " |      The parameter can be accessed as an attribute using given name.\n",
            " |      \n",
            " |      Args:\n",
            " |          name (str): name of the parameter. The parameter can be accessed\n",
            " |              from this module using the given name\n",
            " |          param (Parameter or None): parameter to be added to the module. If\n",
            " |              ``None``, then operations that run on parameters, such as :attr:`cuda`,\n",
            " |              are ignored. If ``None``, the parameter is **not** included in the\n",
            " |              module's :attr:`state_dict`.\n",
            " |  \n",
            " |  register_state_dict_pre_hook(self, hook)\n",
            " |      These hooks will be called with arguments: ``self``, ``prefix``,\n",
            " |      and ``keep_vars`` before calling ``state_dict`` on ``self``. The registered\n",
            " |      hooks can be used to perform pre-processing before the ``state_dict``\n",
            " |      call is made.\n",
            " |  \n",
            " |  requires_grad_(self: ~T, requires_grad: bool = True) -> ~T\n",
            " |      Change if autograd should record operations on parameters in this\n",
            " |      module.\n",
            " |      \n",
            " |      This method sets the parameters' :attr:`requires_grad` attributes\n",
            " |      in-place.\n",
            " |      \n",
            " |      This method is helpful for freezing part of the module for finetuning\n",
            " |      or training parts of a model individually (e.g., GAN training).\n",
            " |      \n",
            " |      See :ref:`locally-disable-grad-doc` for a comparison between\n",
            " |      `.requires_grad_()` and several similar mechanisms that may be confused with it.\n",
            " |      \n",
            " |      Args:\n",
            " |          requires_grad (bool): whether autograd should record operations on\n",
            " |                                parameters in this module. Default: ``True``.\n",
            " |      \n",
            " |      Returns:\n",
            " |          Module: self\n",
            " |  \n",
            " |  set_extra_state(self, state: Any)\n",
            " |      This function is called from :func:`load_state_dict` to handle any extra state\n",
            " |      found within the `state_dict`. Implement this function and a corresponding\n",
            " |      :func:`get_extra_state` for your module if you need to store extra state within its\n",
            " |      `state_dict`.\n",
            " |      \n",
            " |      Args:\n",
            " |          state (dict): Extra state from the `state_dict`\n",
            " |  \n",
            " |  share_memory(self: ~T) -> ~T\n",
            " |      See :meth:`torch.Tensor.share_memory_`\n",
            " |  \n",
            " |  state_dict(self, *args, destination=None, prefix='', keep_vars=False)\n",
            " |      Returns a dictionary containing references to the whole state of the module.\n",
            " |      \n",
            " |      Both parameters and persistent buffers (e.g. running averages) are\n",
            " |      included. Keys are corresponding parameter and buffer names.\n",
            " |      Parameters and buffers set to ``None`` are not included.\n",
            " |      \n",
            " |      .. note::\n",
            " |          The returned object is a shallow copy. It contains references\n",
            " |          to the module's parameters and buffers.\n",
            " |      \n",
            " |      .. warning::\n",
            " |          Currently ``state_dict()`` also accepts positional arguments for\n",
            " |          ``destination``, ``prefix`` and ``keep_vars`` in order. However,\n",
            " |          this is being deprecated and keyword arguments will be enforced in\n",
            " |          future releases.\n",
            " |      \n",
            " |      .. warning::\n",
            " |          Please avoid the use of argument ``destination`` as it is not\n",
            " |          designed for end-users.\n",
            " |      \n",
            " |      Args:\n",
            " |          destination (dict, optional): If provided, the state of module will\n",
            " |              be updated into the dict and the same object is returned.\n",
            " |              Otherwise, an ``OrderedDict`` will be created and returned.\n",
            " |              Default: ``None``.\n",
            " |          prefix (str, optional): a prefix added to parameter and buffer\n",
            " |              names to compose the keys in state_dict. Default: ``''``.\n",
            " |          keep_vars (bool, optional): by default the :class:`~torch.Tensor` s\n",
            " |              returned in the state dict are detached from autograd. If it's\n",
            " |              set to ``True``, detaching will not be performed.\n",
            " |              Default: ``False``.\n",
            " |      \n",
            " |      Returns:\n",
            " |          dict:\n",
            " |              a dictionary containing a whole state of the module\n",
            " |      \n",
            " |      Example::\n",
            " |      \n",
            " |          >>> # xdoctest: +SKIP(\"undefined vars\")\n",
            " |          >>> module.state_dict().keys()\n",
            " |          ['bias', 'weight']\n",
            " |  \n",
            " |  to(self, *args, **kwargs)\n",
            " |      Moves and/or casts the parameters and buffers.\n",
            " |      \n",
            " |      This can be called as\n",
            " |      \n",
            " |      .. function:: to(device=None, dtype=None, non_blocking=False)\n",
            " |         :noindex:\n",
            " |      \n",
            " |      .. function:: to(dtype, non_blocking=False)\n",
            " |         :noindex:\n",
            " |      \n",
            " |      .. function:: to(tensor, non_blocking=False)\n",
            " |         :noindex:\n",
            " |      \n",
            " |      .. function:: to(memory_format=torch.channels_last)\n",
            " |         :noindex:\n",
            " |      \n",
            " |      Its signature is similar to :meth:`torch.Tensor.to`, but only accepts\n",
            " |      floating point or complex :attr:`dtype`\\ s. In addition, this method will\n",
            " |      only cast the floating point or complex parameters and buffers to :attr:`dtype`\n",
            " |      (if given). The integral parameters and buffers will be moved\n",
            " |      :attr:`device`, if that is given, but with dtypes unchanged. When\n",
            " |      :attr:`non_blocking` is set, it tries to convert/move asynchronously\n",
            " |      with respect to the host if possible, e.g., moving CPU Tensors with\n",
            " |      pinned memory to CUDA devices.\n",
            " |      \n",
            " |      See below for examples.\n",
            " |      \n",
            " |      .. note::\n",
            " |          This method modifies the module in-place.\n",
            " |      \n",
            " |      Args:\n",
            " |          device (:class:`torch.device`): the desired device of the parameters\n",
            " |              and buffers in this module\n",
            " |          dtype (:class:`torch.dtype`): the desired floating point or complex dtype of\n",
            " |              the parameters and buffers in this module\n",
            " |          tensor (torch.Tensor): Tensor whose dtype and device are the desired\n",
            " |              dtype and device for all parameters and buffers in this module\n",
            " |          memory_format (:class:`torch.memory_format`): the desired memory\n",
            " |              format for 4D parameters and buffers in this module (keyword\n",
            " |              only argument)\n",
            " |      \n",
            " |      Returns:\n",
            " |          Module: self\n",
            " |      \n",
            " |      Examples::\n",
            " |      \n",
            " |          >>> # xdoctest: +IGNORE_WANT(\"non-deterministic\")\n",
            " |          >>> linear = nn.Linear(2, 2)\n",
            " |          >>> linear.weight\n",
            " |          Parameter containing:\n",
            " |          tensor([[ 0.1913, -0.3420],\n",
            " |                  [-0.5113, -0.2325]])\n",
            " |          >>> linear.to(torch.double)\n",
            " |          Linear(in_features=2, out_features=2, bias=True)\n",
            " |          >>> linear.weight\n",
            " |          Parameter containing:\n",
            " |          tensor([[ 0.1913, -0.3420],\n",
            " |                  [-0.5113, -0.2325]], dtype=torch.float64)\n",
            " |          >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA1)\n",
            " |          >>> gpu1 = torch.device(\"cuda:1\")\n",
            " |          >>> linear.to(gpu1, dtype=torch.half, non_blocking=True)\n",
            " |          Linear(in_features=2, out_features=2, bias=True)\n",
            " |          >>> linear.weight\n",
            " |          Parameter containing:\n",
            " |          tensor([[ 0.1914, -0.3420],\n",
            " |                  [-0.5112, -0.2324]], dtype=torch.float16, device='cuda:1')\n",
            " |          >>> cpu = torch.device(\"cpu\")\n",
            " |          >>> linear.to(cpu)\n",
            " |          Linear(in_features=2, out_features=2, bias=True)\n",
            " |          >>> linear.weight\n",
            " |          Parameter containing:\n",
            " |          tensor([[ 0.1914, -0.3420],\n",
            " |                  [-0.5112, -0.2324]], dtype=torch.float16)\n",
            " |      \n",
            " |          >>> linear = nn.Linear(2, 2, bias=None).to(torch.cdouble)\n",
            " |          >>> linear.weight\n",
            " |          Parameter containing:\n",
            " |          tensor([[ 0.3741+0.j,  0.2382+0.j],\n",
            " |                  [ 0.5593+0.j, -0.4443+0.j]], dtype=torch.complex128)\n",
            " |          >>> linear(torch.ones(3, 2, dtype=torch.cdouble))\n",
            " |          tensor([[0.6122+0.j, 0.1150+0.j],\n",
            " |                  [0.6122+0.j, 0.1150+0.j],\n",
            " |                  [0.6122+0.j, 0.1150+0.j]], dtype=torch.complex128)\n",
            " |  \n",
            " |  to_empty(self: ~T, *, device: Union[str, torch.device]) -> ~T\n",
            " |      Moves the parameters and buffers to the specified device without copying storage.\n",
            " |      \n",
            " |      Args:\n",
            " |          device (:class:`torch.device`): The desired device of the parameters\n",
            " |              and buffers in this module.\n",
            " |      \n",
            " |      Returns:\n",
            " |          Module: self\n",
            " |  \n",
            " |  train(self: ~T, mode: bool = True) -> ~T\n",
            " |      Sets the module in training mode.\n",
            " |      \n",
            " |      This has any effect only on certain modules. See documentations of\n",
            " |      particular modules for details of their behaviors in training/evaluation\n",
            " |      mode, if they are affected, e.g. :class:`Dropout`, :class:`BatchNorm`,\n",
            " |      etc.\n",
            " |      \n",
            " |      Args:\n",
            " |          mode (bool): whether to set training mode (``True``) or evaluation\n",
            " |                       mode (``False``). Default: ``True``.\n",
            " |      \n",
            " |      Returns:\n",
            " |          Module: self\n",
            " |  \n",
            " |  type(self: ~T, dst_type: Union[torch.dtype, str]) -> ~T\n",
            " |      Casts all parameters and buffers to :attr:`dst_type`.\n",
            " |      \n",
            " |      .. note::\n",
            " |          This method modifies the module in-place.\n",
            " |      \n",
            " |      Args:\n",
            " |          dst_type (type or string): the desired type\n",
            " |      \n",
            " |      Returns:\n",
            " |          Module: self\n",
            " |  \n",
            " |  xpu(self: ~T, device: Union[int, torch.device, NoneType] = None) -> ~T\n",
            " |      Moves all model parameters and buffers to the XPU.\n",
            " |      \n",
            " |      This also makes associated parameters and buffers different objects. So\n",
            " |      it should be called before constructing optimizer if the module will\n",
            " |      live on XPU while being optimized.\n",
            " |      \n",
            " |      .. note::\n",
            " |          This method modifies the module in-place.\n",
            " |      \n",
            " |      Arguments:\n",
            " |          device (int, optional): if specified, all parameters will be\n",
            " |              copied to that device\n",
            " |      \n",
            " |      Returns:\n",
            " |          Module: self\n",
            " |  \n",
            " |  zero_grad(self, set_to_none: bool = True) -> None\n",
            " |      Sets gradients of all model parameters to zero. See similar function\n",
            " |      under :class:`torch.optim.Optimizer` for more context.\n",
            " |      \n",
            " |      Args:\n",
            " |          set_to_none (bool): instead of setting to zero, set the grads to None.\n",
            " |              See :meth:`torch.optim.Optimizer.zero_grad` for details.\n",
            " |  \n",
            " |  ----------------------------------------------------------------------\n",
            " |  Data descriptors inherited from torch.nn.modules.module.Module:\n",
            " |  \n",
            " |  __dict__\n",
            " |      dictionary for instance variables (if defined)\n",
            " |  \n",
            " |  __weakref__\n",
            " |      list of weak references to the object (if defined)\n",
            " |  \n",
            " |  ----------------------------------------------------------------------\n",
            " |  Data and other attributes inherited from torch.nn.modules.module.Module:\n",
            " |  \n",
            " |  T_destination = ~T_destination\n",
            " |  \n",
            " |  call_super_init = False\n",
            " |  \n",
            " |  dump_patches = False\n",
            "\n"
          ]
        }
      ],
      "source": [
        "help(ICILoss)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GOvsMDP7t2b2"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
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  "nbformat": 4,
  "nbformat_minor": 0
}