postprocess_eval_f.ipynb

{
  "cells": [
    {
      "metadata": {
        "id": "36fb3d2690aade1"
      },
      "cell_type": "markdown",
      "source": [
        "## Install Dependencies"
      ],
      "id": "36fb3d2690aade1"
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "initial_id",
      "metadata": {
        "collapsed": true,
        "id": "initial_id",
        "outputId": "1b3ba42f-5131-41fb-da2c-4486f79b6f2f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://download.pytorch.org/whl/cu126\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.11/dist-packages (2.6.0+cu124)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.11/dist-packages (0.21.0+cu124)\n",
            "Requirement already satisfied: torchaudio in /usr/local/lib/python3.11/dist-packages (2.6.0+cu124)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from torch) (3.18.0)\n",
            "Requirement already satisfied: typing-extensions>=4.10.0 in /usr/local/lib/python3.11/dist-packages (from torch) (4.13.1)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.11/dist-packages (from torch) (3.4.2)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.11/dist-packages (from torch) (3.1.6)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.11/dist-packages (from torch) (2025.3.2)\n",
            "Collecting nvidia-cuda-nvrtc-cu12==12.4.127 (from torch)\n",
            "  Downloading https://download.pytorch.org/whl/cu126/nvidia_cuda_nvrtc_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
            "Collecting nvidia-cuda-runtime-cu12==12.4.127 (from torch)\n",
            "  Downloading https://download.pytorch.org/whl/cu126/nvidia_cuda_runtime_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
            "Collecting nvidia-cuda-cupti-cu12==12.4.127 (from torch)\n",
            "  Downloading https://download.pytorch.org/whl/cu126/nvidia_cuda_cupti_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n",
            "Collecting nvidia-cudnn-cu12==9.1.0.70 (from torch)\n",
            "  Downloading https://download.pytorch.org/whl/cu126/nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n",
            "Collecting nvidia-cublas-cu12==12.4.5.8 (from torch)\n",
            "  Downloading https://download.pytorch.org/whl/cu126/nvidia_cublas_cu12-12.4.5.8-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
            "Collecting nvidia-cufft-cu12==11.2.1.3 (from torch)\n",
            "  Downloading https://download.pytorch.org/whl/cu126/nvidia_cufft_cu12-11.2.1.3-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
            "Collecting nvidia-curand-cu12==10.3.5.147 (from torch)\n",
            "  Downloading https://download.pytorch.org/whl/cu126/nvidia_curand_cu12-10.3.5.147-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
            "Collecting nvidia-cusolver-cu12==11.6.1.9 (from torch)\n",
            "  Downloading https://download.pytorch.org/whl/cu126/nvidia_cusolver_cu12-11.6.1.9-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n",
            "Collecting nvidia-cusparse-cu12==12.3.1.170 (from torch)\n",
            "  Downloading https://download.pytorch.org/whl/cu126/nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n",
            "Requirement already satisfied: nvidia-cusparselt-cu12==0.6.2 in /usr/local/lib/python3.11/dist-packages (from torch) (0.6.2)\n",
            "Requirement already satisfied: nvidia-nccl-cu12==2.21.5 in /usr/local/lib/python3.11/dist-packages (from torch) (2.21.5)\n",
            "Requirement already satisfied: nvidia-nvtx-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch) (12.4.127)\n",
            "Collecting nvidia-nvjitlink-cu12==12.4.127 (from torch)\n",
            "  Downloading https://download.pytorch.org/whl/cu126/nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
            "Requirement already satisfied: triton==3.2.0 in /usr/local/lib/python3.11/dist-packages (from torch) (3.2.0)\n",
            "Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.11/dist-packages (from torch) (1.13.1)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from sympy==1.13.1->torch) (1.3.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (from torchvision) (2.0.2)\n",
            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.11/dist-packages (from torchvision) (11.1.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.11/dist-packages (from jinja2->torch) (3.0.2)\n",
            "Downloading https://download.pytorch.org/whl/cu126/nvidia_cublas_cu12-12.4.5.8-py3-none-manylinux2014_x86_64.whl (363.4 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m363.4/363.4 MB\u001b[0m \u001b[31m4.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading https://download.pytorch.org/whl/cu126/nvidia_cuda_cupti_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (13.8 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.8/13.8 MB\u001b[0m \u001b[31m68.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading https://download.pytorch.org/whl/cu126/nvidia_cuda_nvrtc_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (24.6 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m24.6/24.6 MB\u001b[0m \u001b[31m69.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading https://download.pytorch.org/whl/cu126/nvidia_cuda_runtime_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (883 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m883.7/883.7 kB\u001b[0m \u001b[31m48.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading https://download.pytorch.org/whl/cu126/nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl (664.8 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m664.8/664.8 MB\u001b[0m \u001b[31m1.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading https://download.pytorch.org/whl/cu126/nvidia_cufft_cu12-11.2.1.3-py3-none-manylinux2014_x86_64.whl (211.5 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m211.5/211.5 MB\u001b[0m \u001b[31m6.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading https://download.pytorch.org/whl/cu126/nvidia_curand_cu12-10.3.5.147-py3-none-manylinux2014_x86_64.whl (56.3 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.3/56.3 MB\u001b[0m \u001b[31m12.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading https://download.pytorch.org/whl/cu126/nvidia_cusolver_cu12-11.6.1.9-py3-none-manylinux2014_x86_64.whl (127.9 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m127.9/127.9 MB\u001b[0m \u001b[31m7.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading https://download.pytorch.org/whl/cu126/nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_x86_64.whl (207.5 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m207.5/207.5 MB\u001b[0m \u001b[31m6.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading https://download.pytorch.org/whl/cu126/nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (21.1 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.1/21.1 MB\u001b[0m \u001b[31m92.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: nvidia-nvjitlink-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12\n",
            "  Attempting uninstall: nvidia-nvjitlink-cu12\n",
            "    Found existing installation: nvidia-nvjitlink-cu12 12.5.82\n",
            "    Uninstalling nvidia-nvjitlink-cu12-12.5.82:\n",
            "      Successfully uninstalled nvidia-nvjitlink-cu12-12.5.82\n",
            "  Attempting uninstall: nvidia-curand-cu12\n",
            "    Found existing installation: nvidia-curand-cu12 10.3.6.82\n",
            "    Uninstalling nvidia-curand-cu12-10.3.6.82:\n",
            "      Successfully uninstalled nvidia-curand-cu12-10.3.6.82\n",
            "  Attempting uninstall: nvidia-cufft-cu12\n",
            "    Found existing installation: nvidia-cufft-cu12 11.2.3.61\n",
            "    Uninstalling nvidia-cufft-cu12-11.2.3.61:\n",
            "      Successfully uninstalled nvidia-cufft-cu12-11.2.3.61\n",
            "  Attempting uninstall: nvidia-cuda-runtime-cu12\n",
            "    Found existing installation: nvidia-cuda-runtime-cu12 12.5.82\n",
            "    Uninstalling nvidia-cuda-runtime-cu12-12.5.82:\n",
            "      Successfully uninstalled nvidia-cuda-runtime-cu12-12.5.82\n",
            "  Attempting uninstall: nvidia-cuda-nvrtc-cu12\n",
            "    Found existing installation: nvidia-cuda-nvrtc-cu12 12.5.82\n",
            "    Uninstalling nvidia-cuda-nvrtc-cu12-12.5.82:\n",
            "      Successfully uninstalled nvidia-cuda-nvrtc-cu12-12.5.82\n",
            "  Attempting uninstall: nvidia-cuda-cupti-cu12\n",
            "    Found existing installation: nvidia-cuda-cupti-cu12 12.5.82\n",
            "    Uninstalling nvidia-cuda-cupti-cu12-12.5.82:\n",
            "      Successfully uninstalled nvidia-cuda-cupti-cu12-12.5.82\n",
            "  Attempting uninstall: nvidia-cublas-cu12\n",
            "    Found existing installation: nvidia-cublas-cu12 12.5.3.2\n",
            "    Uninstalling nvidia-cublas-cu12-12.5.3.2:\n",
            "      Successfully uninstalled nvidia-cublas-cu12-12.5.3.2\n",
            "  Attempting uninstall: nvidia-cusparse-cu12\n",
            "    Found existing installation: nvidia-cusparse-cu12 12.5.1.3\n",
            "    Uninstalling nvidia-cusparse-cu12-12.5.1.3:\n",
            "      Successfully uninstalled nvidia-cusparse-cu12-12.5.1.3\n",
            "  Attempting uninstall: nvidia-cudnn-cu12\n",
            "    Found existing installation: nvidia-cudnn-cu12 9.3.0.75\n",
            "    Uninstalling nvidia-cudnn-cu12-9.3.0.75:\n",
            "      Successfully uninstalled nvidia-cudnn-cu12-9.3.0.75\n",
            "  Attempting uninstall: nvidia-cusolver-cu12\n",
            "    Found existing installation: nvidia-cusolver-cu12 11.6.3.83\n",
            "    Uninstalling nvidia-cusolver-cu12-11.6.3.83:\n",
            "      Successfully uninstalled nvidia-cusolver-cu12-11.6.3.83\n",
            "Successfully installed nvidia-cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia-cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-nvjitlink-cu12-12.4.127\n"
          ]
        }
      ],
      "source": [
        "!pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126"
      ]
    },
    {
      "metadata": {
        "id": "feebbc5d019069e0",
        "outputId": "0bb1b45b-5aaa-4e7c-854e-5676450cb313",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "cell_type": "code",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting nnunetv2\n",
            "  Downloading nnunetv2-2.6.0.tar.gz (206 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/206.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m206.3/206.3 kB\u001b[0m \u001b[31m6.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting acvl-utils\n",
            "  Downloading acvl_utils-0.2.5.tar.gz (29 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: cupy-cuda12x in /usr/local/lib/python3.11/dist-packages (13.3.0)\n",
            "Requirement already satisfied: torch>=2.1.2 in /usr/local/lib/python3.11/dist-packages (from nnunetv2) (2.6.0+cu124)\n",
            "Collecting dynamic-network-architectures<0.4,>=0.3.1 (from nnunetv2)\n",
            "  Downloading dynamic_network_architectures-0.3.1.tar.gz (20 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.11/dist-packages (from nnunetv2) (4.67.1)\n",
            "Collecting dicom2nifti (from nnunetv2)\n",
            "  Downloading dicom2nifti-2.6.0-py3-none-any.whl.metadata (1.5 kB)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.11/dist-packages (from nnunetv2) (1.14.1)\n",
            "Collecting batchgenerators>=0.25.1 (from nnunetv2)\n",
            "  Downloading batchgenerators-0.25.1.tar.gz (76 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.0/77.0 kB\u001b[0m \u001b[31m5.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: numpy>=1.24 in /usr/local/lib/python3.11/dist-packages (from nnunetv2) (2.0.2)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.11/dist-packages (from nnunetv2) (1.6.1)\n",
            "Requirement already satisfied: scikit-image>=0.19.3 in /usr/local/lib/python3.11/dist-packages (from nnunetv2) (0.25.2)\n",
            "Collecting SimpleITK>=2.2.1 (from nnunetv2)\n",
            "  Downloading SimpleITK-2.4.1-cp311-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (7.9 kB)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.11/dist-packages (from nnunetv2) (2.2.2)\n",
            "Requirement already satisfied: graphviz in /usr/local/lib/python3.11/dist-packages (from nnunetv2) (0.20.3)\n",
            "Requirement already satisfied: tifffile in /usr/local/lib/python3.11/dist-packages (from nnunetv2) (2025.3.30)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.11/dist-packages (from nnunetv2) (2.32.3)\n",
            "Requirement already satisfied: nibabel in /usr/local/lib/python3.11/dist-packages (from nnunetv2) (5.3.2)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.11/dist-packages (from nnunetv2) (3.10.0)\n",
            "Requirement already satisfied: seaborn in /usr/local/lib/python3.11/dist-packages (from nnunetv2) (0.13.2)\n",
            "Collecting imagecodecs (from nnunetv2)\n",
            "  Downloading imagecodecs-2025.3.30-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (20 kB)\n",
            "Collecting yacs (from nnunetv2)\n",
            "  Downloading yacs-0.1.8-py3-none-any.whl.metadata (639 bytes)\n",
            "Collecting batchgeneratorsv2>=0.2 (from nnunetv2)\n",
            "  Downloading batchgeneratorsv2-0.2.3.tar.gz (35 kB)\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: einops in /usr/local/lib/python3.11/dist-packages (from nnunetv2) (0.8.1)\n",
            "Requirement already satisfied: blosc2>=3.0.0b1 in /usr/local/lib/python3.11/dist-packages (from nnunetv2) (3.3.0)\n",
            "Collecting connected-components-3d (from acvl-utils)\n",
            "  Downloading connected_components_3d-3.23.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (32 kB)\n",
            "Requirement already satisfied: fastrlock>=0.5 in /usr/local/lib/python3.11/dist-packages (from cupy-cuda12x) (0.8.3)\n",
            "Requirement already satisfied: pillow>=7.1.2 in /usr/local/lib/python3.11/dist-packages (from batchgenerators>=0.25.1->nnunetv2) (11.1.0)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.11/dist-packages (from batchgenerators>=0.25.1->nnunetv2) (1.0.0)\n",
            "Collecting unittest2 (from batchgenerators>=0.25.1->nnunetv2)\n",
            "  Downloading unittest2-1.1.0-py2.py3-none-any.whl.metadata (15 kB)\n",
            "Requirement already satisfied: threadpoolctl in /usr/local/lib/python3.11/dist-packages (from batchgenerators>=0.25.1->nnunetv2) (3.6.0)\n",
            "Collecting fft-conv-pytorch (from batchgeneratorsv2>=0.2->nnunetv2)\n",
            "  Downloading fft_conv_pytorch-1.2.0-py3-none-any.whl.metadata (2.8 kB)\n",
            "Requirement already satisfied: ndindex in /usr/local/lib/python3.11/dist-packages (from blosc2>=3.0.0b1->nnunetv2) (1.9.2)\n",
            "Requirement already satisfied: msgpack in /usr/local/lib/python3.11/dist-packages (from blosc2>=3.0.0b1->nnunetv2) (1.1.0)\n",
            "Requirement already satisfied: platformdirs in /usr/local/lib/python3.11/dist-packages (from blosc2>=3.0.0b1->nnunetv2) (4.3.7)\n",
            "Requirement already satisfied: numexpr in /usr/local/lib/python3.11/dist-packages (from blosc2>=3.0.0b1->nnunetv2) (2.10.2)\n",
            "Requirement already satisfied: py-cpuinfo in /usr/local/lib/python3.11/dist-packages (from blosc2>=3.0.0b1->nnunetv2) (9.0.0)\n",
            "Requirement already satisfied: networkx>=3.0 in /usr/local/lib/python3.11/dist-packages (from scikit-image>=0.19.3->nnunetv2) (3.4.2)\n",
            "Requirement already satisfied: imageio!=2.35.0,>=2.33 in /usr/local/lib/python3.11/dist-packages (from scikit-image>=0.19.3->nnunetv2) (2.37.0)\n",
            "Requirement already satisfied: packaging>=21 in /usr/local/lib/python3.11/dist-packages (from scikit-image>=0.19.3->nnunetv2) (24.2)\n",
            "Requirement already satisfied: lazy-loader>=0.4 in /usr/local/lib/python3.11/dist-packages (from scikit-image>=0.19.3->nnunetv2) (0.4)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from torch>=2.1.2->nnunetv2) (3.18.0)\n",
            "Requirement already satisfied: typing-extensions>=4.10.0 in /usr/local/lib/python3.11/dist-packages (from torch>=2.1.2->nnunetv2) (4.13.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.11/dist-packages (from torch>=2.1.2->nnunetv2) (3.1.6)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.11/dist-packages (from torch>=2.1.2->nnunetv2) (2025.3.2)\n",
            "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch>=2.1.2->nnunetv2) (12.4.127)\n",
            "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch>=2.1.2->nnunetv2) (12.4.127)\n",
            "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch>=2.1.2->nnunetv2) (12.4.127)\n",
            "Requirement already satisfied: nvidia-cudnn-cu12==9.1.0.70 in /usr/local/lib/python3.11/dist-packages (from torch>=2.1.2->nnunetv2) (9.1.0.70)\n",
            "Requirement already satisfied: nvidia-cublas-cu12==12.4.5.8 in /usr/local/lib/python3.11/dist-packages (from torch>=2.1.2->nnunetv2) (12.4.5.8)\n",
            "Requirement already satisfied: nvidia-cufft-cu12==11.2.1.3 in /usr/local/lib/python3.11/dist-packages (from torch>=2.1.2->nnunetv2) (11.2.1.3)\n",
            "Requirement already satisfied: nvidia-curand-cu12==10.3.5.147 in /usr/local/lib/python3.11/dist-packages (from torch>=2.1.2->nnunetv2) (10.3.5.147)\n",
            "Requirement already satisfied: nvidia-cusolver-cu12==11.6.1.9 in /usr/local/lib/python3.11/dist-packages (from torch>=2.1.2->nnunetv2) (11.6.1.9)\n",
            "Requirement already satisfied: nvidia-cusparse-cu12==12.3.1.170 in /usr/local/lib/python3.11/dist-packages (from torch>=2.1.2->nnunetv2) (12.3.1.170)\n",
            "Requirement already satisfied: nvidia-cusparselt-cu12==0.6.2 in /usr/local/lib/python3.11/dist-packages (from torch>=2.1.2->nnunetv2) (0.6.2)\n",
            "Requirement already satisfied: nvidia-nccl-cu12==2.21.5 in /usr/local/lib/python3.11/dist-packages (from torch>=2.1.2->nnunetv2) (2.21.5)\n",
            "Requirement already satisfied: nvidia-nvtx-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch>=2.1.2->nnunetv2) (12.4.127)\n",
            "Requirement already satisfied: nvidia-nvjitlink-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch>=2.1.2->nnunetv2) (12.4.127)\n",
            "Requirement already satisfied: triton==3.2.0 in /usr/local/lib/python3.11/dist-packages (from torch>=2.1.2->nnunetv2) (3.2.0)\n",
            "Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.11/dist-packages (from torch>=2.1.2->nnunetv2) (1.13.1)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from sympy==1.13.1->torch>=2.1.2->nnunetv2) (1.3.0)\n",
            "Collecting pydicom>=2.2.0 (from dicom2nifti->nnunetv2)\n",
            "  Downloading pydicom-3.0.1-py3-none-any.whl.metadata (9.4 kB)\n",
            "Collecting python-gdcm (from dicom2nifti->nnunetv2)\n",
            "  Downloading python_gdcm-3.0.24.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (3.7 kB)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->nnunetv2) (1.3.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib->nnunetv2) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib->nnunetv2) (4.57.0)\n",
            "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->nnunetv2) (1.4.8)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib->nnunetv2) (3.2.3)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib->nnunetv2) (2.8.2)\n",
            "Requirement already satisfied: importlib-resources>=5.12 in /usr/local/lib/python3.11/dist-packages (from nibabel->nnunetv2) (6.5.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas->nnunetv2) (2025.2)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas->nnunetv2) (2025.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests->nnunetv2) (3.4.1)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests->nnunetv2) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests->nnunetv2) (2.3.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests->nnunetv2) (2025.1.31)\n",
            "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn->nnunetv2) (1.4.2)\n",
            "Requirement already satisfied: PyYAML in /usr/local/lib/python3.11/dist-packages (from yacs->nnunetv2) (6.0.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.7->matplotlib->nnunetv2) (1.17.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.11/dist-packages (from jinja2->torch>=2.1.2->nnunetv2) (3.0.2)\n",
            "Collecting argparse (from unittest2->batchgenerators>=0.25.1->nnunetv2)\n",
            "  Downloading argparse-1.4.0-py2.py3-none-any.whl.metadata (2.8 kB)\n",
            "Collecting traceback2 (from unittest2->batchgenerators>=0.25.1->nnunetv2)\n",
            "  Downloading traceback2-1.4.0-py2.py3-none-any.whl.metadata (1.5 kB)\n",
            "Collecting linecache2 (from traceback2->unittest2->batchgenerators>=0.25.1->nnunetv2)\n",
            "  Downloading linecache2-1.0.0-py2.py3-none-any.whl.metadata (1000 bytes)\n",
            "Downloading SimpleITK-2.4.1-cp311-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (52.3 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m52.3/52.3 MB\u001b[0m \u001b[31m12.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading connected_components_3d-3.23.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.3 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.3/4.3 MB\u001b[0m \u001b[31m76.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading dicom2nifti-2.6.0-py3-none-any.whl (43 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.6/43.6 kB\u001b[0m \u001b[31m2.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading imagecodecs-2025.3.30-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (45.6 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m45.6/45.6 MB\u001b[0m \u001b[31m11.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading yacs-0.1.8-py3-none-any.whl (14 kB)\n",
            "Downloading pydicom-3.0.1-py3-none-any.whl (2.4 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.4/2.4 MB\u001b[0m \u001b[31m80.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading fft_conv_pytorch-1.2.0-py3-none-any.whl (6.8 kB)\n",
            "Downloading python_gdcm-3.0.24.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (13.1 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.1/13.1 MB\u001b[0m \u001b[31m82.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading unittest2-1.1.0-py2.py3-none-any.whl (96 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m96.4/96.4 kB\u001b[0m \u001b[31m9.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading argparse-1.4.0-py2.py3-none-any.whl (23 kB)\n",
            "Downloading traceback2-1.4.0-py2.py3-none-any.whl (16 kB)\n",
            "Downloading linecache2-1.0.0-py2.py3-none-any.whl (12 kB)\n",
            "Building wheels for collected packages: nnunetv2, acvl-utils, batchgenerators, batchgeneratorsv2, dynamic-network-architectures\n",
            "  Building wheel for nnunetv2 (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for nnunetv2: filename=nnunetv2-2.6.0-py3-none-any.whl size=277014 sha256=6aa2550c70bcf9ff84277d1ea4ee999c83b28b8373ae174422821db3d1de68df\n",
            "  Stored in directory: /root/.cache/pip/wheels/3d/0d/bb/f932cb0032d1aacc2f336de421448313d147db8eed671096e9\n",
            "  Building wheel for acvl-utils (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for acvl-utils: filename=acvl_utils-0.2.5-py3-none-any.whl size=27213 sha256=60f5762f322f9b67b786a43caec4fa4ed142f1cfdb27b9008eada9e58c96f01f\n",
            "  Stored in directory: /root/.cache/pip/wheels/3f/8c/10/dcba79e0b2d1d463605233cec1fc6cfad47af5230b8985e464\n",
            "  Building wheel for batchgenerators (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for batchgenerators: filename=batchgenerators-0.25.1-py3-none-any.whl size=93088 sha256=70a5fd446caad62a6d2736deefb629159b77aa775c326f7e53c395d822b4f098\n",
            "  Stored in directory: /root/.cache/pip/wheels/56/11/c7/fadca30e054c602093ffe36ba8a2f0a87dd2f86ac75191d3ed\n",
            "  Building wheel for batchgeneratorsv2 (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for batchgeneratorsv2: filename=batchgeneratorsv2-0.2.3-py3-none-any.whl size=47567 sha256=001e31ff2c50190b2a1ac859b34a09edde9df8820050db9d20f874ae48871dc0\n",
            "  Stored in directory: /root/.cache/pip/wheels/1f/68/57/3a0eceb67b9c1b630df16113639b66a843dfd6686a4ed5ae1d\n",
            "  Building wheel for dynamic-network-architectures (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for dynamic-network-architectures: filename=dynamic_network_architectures-0.3.1-py3-none-any.whl size=30048 sha256=0a8d1b9a15beb6220ae735e2b181a9681b375d63b418f487a5755fa399624210\n",
            "  Stored in directory: /root/.cache/pip/wheels/d9/8f/23/133ba252665b6f93abdbb294b323cc8fec041be83e4d22b701\n",
            "Successfully built nnunetv2 acvl-utils batchgenerators batchgeneratorsv2 dynamic-network-architectures\n",
            "Installing collected packages: SimpleITK, linecache2, argparse, yacs, traceback2, python-gdcm, pydicom, imagecodecs, connected-components-3d, unittest2, dicom2nifti, batchgenerators, fft-conv-pytorch, dynamic-network-architectures, acvl-utils, batchgeneratorsv2, nnunetv2\n",
            "Successfully installed SimpleITK-2.4.1 acvl-utils-0.2.5 argparse-1.4.0 batchgenerators-0.25.1 batchgeneratorsv2-0.2.3 connected-components-3d-3.23.0 dicom2nifti-2.6.0 dynamic-network-architectures-0.3.1 fft-conv-pytorch-1.2.0 imagecodecs-2025.3.30 linecache2-1.0.0 nnunetv2-2.6.0 pydicom-3.0.1 python-gdcm-3.0.24.1 traceback2-1.4.0 unittest2-1.1.0 yacs-0.1.8\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "argparse"
                ]
              },
              "id": "396ba6339a254481a5f686ca1ebe773f"
            }
          },
          "metadata": {}
        }
      ],
      "execution_count": 2,
      "source": [
        "!pip3 install nnunetv2 acvl-utils cupy-cuda12x"
      ],
      "id": "feebbc5d019069e0"
    },
    {
      "metadata": {
        "id": "41c8492b64215436"
      },
      "cell_type": "markdown",
      "source": [
        "## Define Functions"
      ],
      "id": "41c8492b64215436"
    },
    {
      "metadata": {
        "id": "f903ce2dc6a8c980"
      },
      "cell_type": "code",
      "outputs": [],
      "execution_count": 1,
      "source": [
        "from abc import abstractmethod, ABC\n",
        "from typing import Optional, Dict, Union, Tuple, List\n",
        "\n",
        "import numpy as np\n",
        "import cupy as cp\n",
        "import torch\n",
        "from nnunetv2.configuration import ANISO_THRESHOLD\n",
        "from cupyx.scipy import ndimage\n",
        "from nnunetv2.utilities.helpers import empty_cache\n",
        "import gc\n",
        "\n",
        "CT_configuration = {\n",
        "    \"transpose_forward\": [\n",
        "        0,\n",
        "        1,\n",
        "        2\n",
        "    ],\n",
        "    \"spacing\": [\n",
        "                2.5,\n",
        "                0.7958984971046448,\n",
        "                0.7958984971046448\n",
        "            ],\n",
        "    'intensity_prop': {\n",
        "            \"max\": 3071.0,\n",
        "            \"mean\": 97.29716491699219,\n",
        "            \"median\": 118.0,\n",
        "            \"min\": -1024.0,\n",
        "            \"percentile_00_5\": -958.0,\n",
        "            \"percentile_99_5\": 270.0,\n",
        "            \"std\": 137.8484649658203\n",
        "        }}\n",
        "\n",
        "def logit_to_segment(predicted_logits):\n",
        "    max_logit, max_class = torch.max(predicted_logits, dim=0)\n",
        "    segmentation = torch.where(max_logit >= 0.5, max_class, torch.tensor(0, device=predicted_logits.device))\n",
        "\n",
        "    return segmentation\n",
        "\n",
        "def resize_by_chunk(torch_data, new_shape, chunk_size = 300):\n",
        "    torch_data = torch_data.detach().cpu()\n",
        "    torch.cuda.empty_cache()\n",
        "    step = new_shape[0] // chunk_size + 1\n",
        "    seg_old_spacing = np.zeros(new_shape)\n",
        "    z = torch_data.shape[2]\n",
        "    stride = int(z / step)\n",
        "    step1 = [i * stride for i in range(step)] + [z]\n",
        "    z = new_shape[0]\n",
        "    stride = int(z / step)\n",
        "    step2 = [i * stride for i in range(step)] + [z]\n",
        "    for i in range(step):\n",
        "        size = list(new_shape)\n",
        "        size[0] = step2[i + 1] - step2[i]\n",
        "        slicer = torch_data[:,:, step1[i]:step1[i + 1]]#.half()\n",
        "        slicer = torch.nn.functional.interpolate(slicer.cuda(), mode='trilinear', size=size, align_corners=True)[0]\n",
        "        seg_old_spacing[step2[i]:step2[i + 1]] = logit_to_segment(slicer).cpu()\n",
        "        del slicer\n",
        "        torch.cuda.empty_cache()\n",
        "\n",
        "    return torch.from_numpy(seg_old_spacing)\n",
        "\n",
        "def fast_resample_logit_to_shape(torch_data: Union[torch.Tensor, np.ndarray],\n",
        "                                  new_shape: Union[Tuple[int, ...], List[int], np.ndarray],\n",
        "                                  current_spacing: Union[Tuple[float, ...], List[float], np.ndarray],\n",
        "                                  new_spacing: Union[Tuple[float, ...], List[float], np.ndarray],\n",
        "                                  is_seg: bool = False,\n",
        "                                  order: int = 3, order_z: int = 0,\n",
        "                                  force_separate_z: Union[bool, None] = False,\n",
        "                                  separate_z_anisotropy_threshold: float = ANISO_THRESHOLD):\n",
        "    use_gpu = True\n",
        "    device = torch.device(\"cuda\" if use_gpu else \"cpu\")\n",
        "    order_to_mode_map = {\n",
        "        0: \"nearest\",\n",
        "        1: \"trilinear\" if new_shape[0] > 1 else \"bilinear\",\n",
        "        2: \"trilinear\" if new_shape[0] > 1 else \"bilinear\",\n",
        "        3: \"trilinear\" if new_shape[0] > 1 else \"bicubic\",\n",
        "        4: \"trilinear\" if new_shape[0] > 1 else \"bicubic\",\n",
        "        5: \"trilinear\" if new_shape[0] > 1 else \"bicubic\",\n",
        "    }\n",
        "    resize_fn = torch.nn.functional.interpolate\n",
        "    kwargs = {\n",
        "        'mode': order_to_mode_map[order],\n",
        "        'align_corners': False,\n",
        "    }\n",
        "    shape = np.array(torch_data[0].shape)\n",
        "    new_shape = np.array(new_shape)\n",
        "    if np.any(shape != new_shape):\n",
        "        if new_shape[0] == 1:\n",
        "            torch_data = torch_data.transpose(1, 0)\n",
        "            new_shape = new_shape[1:]\n",
        "        else:\n",
        "            torch_data = torch_data.unsqueeze(0)\n",
        "        gc.collect()\n",
        "        empty_cache(device)\n",
        "        if new_shape[0] < 600:\n",
        "            torch_data = resize_fn(torch_data.to(device), tuple(new_shape), **kwargs)\n",
        "\n",
        "            if new_shape[0] == 1:\n",
        "                torch_data = torch_data.transpose(1, 0)\n",
        "            else:\n",
        "                torch_data = torch_data.squeeze(0)\n",
        "        else:\n",
        "            torch_data = resize_by_chunk(torch_data.to(device), tuple(new_shape))\n",
        "        reshaped_final_data = torch_data\n",
        "        return reshaped_final_data\n",
        "    else:\n",
        "        print(\"no resampling necessary\")\n",
        "        return torch_data\n",
        "\n",
        "def convert_predicted_logits_to_segmentation_with_correct_shape(predicted_logits: Union[torch.Tensor, np.ndarray],\n",
        "                                                                properties_dict: dict,\n",
        "                                                                ):\n",
        "\n",
        "    spacing_transposed = [properties_dict['spacing'][i] for i in CT_configuration['transpose_forward']]\n",
        "    current_spacing = CT_configuration['spacing'] if \\\n",
        "        len(CT_configuration['spacing']) == \\\n",
        "        len(properties_dict['shape_after_cropping_and_before_resampling']) else \\\n",
        "        [spacing_transposed[0], *CT_configuration['spacing']]\n",
        "    predicted_logits = fast_resample_logit_to_shape(predicted_logits,\n",
        "                                            properties_dict['shape_after_cropping_and_before_resampling'],\n",
        "                                            current_spacing,\n",
        "                                            [properties_dict['spacing'][i] for i in CT_configuration['transpose_forward']])\n",
        "\n",
        "    segmentation = logit_to_segment(predicted_logits)\n",
        "    return segmentation\n",
        "\n",
        "\n",
        "class ImageNormalization(ABC):\n",
        "    leaves_pixels_outside_mask_at_zero_if_use_mask_for_norm_is_true = None\n",
        "\n",
        "    def __init__(self, use_mask_for_norm: Optional[bool] = None, intensityproperties: Optional[Dict] = None,\n",
        "                 target_dtype: torch.dtype = torch.float32):\n",
        "        assert use_mask_for_norm is None or isinstance(use_mask_for_norm, bool)\n",
        "        self.use_mask_for_norm = use_mask_for_norm\n",
        "        assert isinstance(intensityproperties, dict) or intensityproperties is None\n",
        "        self.intensityproperties = intensityproperties\n",
        "        self.target_dtype = target_dtype\n",
        "\n",
        "    @abstractmethod\n",
        "    def run(self, image: torch.Tensor, seg: Optional[torch.Tensor] = None) -> torch.Tensor:\n",
        "        raise NotImplementedError\n",
        "\n",
        "\n",
        "class CTNormalization(ImageNormalization):\n",
        "    leaves_pixels_outside_mask_at_zero_if_use_mask_for_norm_is_true = False\n",
        "\n",
        "    def run(self, image: torch.Tensor, seg: Optional[torch.Tensor] = None) -> torch.Tensor:\n",
        "        assert self.intensityproperties is not None, \"CTNormalization requires intensity properties\"\n",
        "        mean_intensity = self.intensityproperties['mean']\n",
        "        std_intensity = self.intensityproperties['std']\n",
        "        lower_bound = self.intensityproperties['percentile_00_5']\n",
        "        upper_bound = self.intensityproperties['percentile_99_5']\n",
        "\n",
        "        image = image.to(dtype=self.target_dtype)\n",
        "        image = torch.clamp(image, lower_bound, upper_bound)\n",
        "        image = (image - mean_intensity) / max(std_intensity, 1e-8)\n",
        "        return image\n",
        "\n",
        "\n",
        "def create_nonzero_mask(data):\n",
        "    \"\"\"\n",
        "\n",
        "    :param data:\n",
        "    :return: the mask is True where the data is nonzero\n",
        "    \"\"\"\n",
        "    assert data.ndim in (3, 4), \"data must have shape (C, X, Y, Z) or shape (C, X, Y)\"\n",
        "    nonzero_mask = data[0] != 0\n",
        "    for c in range(1, data.shape[0]):\n",
        "        nonzero_mask |= data[c] != 0\n",
        "    filled_mask = ndimage.binary_fill_holes(nonzero_mask)\n",
        "    return filled_mask\n",
        "\n",
        "\n",
        "def get_bbox_from_mask(mask: cp.ndarray) -> List[List[int]]:\n",
        "    \"\"\"\n",
        "    ALL bounding boxes in acvl_utils and nnU-Netv2 are half open interval [start, end)!\n",
        "    - Alignment with Python Slicing\n",
        "    - Ease of Subdivision\n",
        "    - Consistency in Multi-Dimensional Arrays\n",
        "    - Precedent in Computer Graphics\n",
        "\n",
        "    This implementation uses CuPy for GPU acceleration. The mask should be a CuPy array.\n",
        "\n",
        "    Args:\n",
        "        mask (cp.ndarray): 3D mask array on GPU\n",
        "\n",
        "    Returns:\n",
        "        List[List[int]]: Bounding box coordinates as [[minz, maxz], [minx, maxx], [miny, maxy]]\n",
        "    \"\"\"\n",
        "    Z, X, Y = mask.shape\n",
        "    minzidx, maxzidx, minxidx, maxxidx, minyidx, maxyidx = 0, Z, 0, X, 0, Y\n",
        "\n",
        "    # Create range arrays on GPU\n",
        "    zidx = cp.arange(Z)\n",
        "    xidx = cp.arange(X)\n",
        "    yidx = cp.arange(Y)\n",
        "\n",
        "    # Z dimension\n",
        "    for z in zidx.get():  # .get() to iterate over CPU array\n",
        "        if cp.any(mask[z]).get():  # .get() to get boolean result to CPU\n",
        "            minzidx = z\n",
        "            break\n",
        "    for z in zidx[::-1].get():\n",
        "        if cp.any(mask[z]).get():\n",
        "            maxzidx = z + 1\n",
        "            break\n",
        "\n",
        "    # X dimension\n",
        "    for x in xidx.get():\n",
        "        if cp.any(mask[:, x]).get():\n",
        "            minxidx = x\n",
        "            break\n",
        "    for x in xidx[::-1].get():\n",
        "        if cp.any(mask[:, x]).get():\n",
        "            maxxidx = x + 1\n",
        "            break\n",
        "\n",
        "    # Y dimension\n",
        "    for y in yidx.get():\n",
        "        if cp.any(mask[:, :, y]).get():\n",
        "            minyidx = y\n",
        "            break\n",
        "    for y in yidx[::-1].get():\n",
        "        if cp.any(mask[:, :, y]).get():\n",
        "            maxyidx = y + 1\n",
        "            break\n",
        "\n",
        "    return [[minzidx, maxzidx], [minxidx, maxxidx], [minyidx, maxyidx]]\n",
        "\n",
        "def bounding_box_to_slice(bounding_box: List[List[int]]):\n",
        "    \"\"\"\n",
        "    ALL bounding boxes in acvl_utils and nnU-Netv2 are half open interval [start, end)!\n",
        "    - Alignment with Python Slicing\n",
        "    - Ease of Subdivision\n",
        "    - Consistency in Multi-Dimensional Arrays\n",
        "    - Precedent in Computer Graphics\n",
        "    https://chatgpt.com/share/679203ec-3fbc-8013-a003-13a7adfb1e73\n",
        "    \"\"\"\n",
        "    return tuple([slice(*i) for i in bounding_box])\n",
        "\n",
        "\n",
        "def crop_to_nonzero(data, seg=None, nonzero_label=-1):\n",
        "    \"\"\"\n",
        "\n",
        "    :param data:\n",
        "    :param seg:\n",
        "    :param nonzero_label: this will be written into the segmentation map\n",
        "    :return:\n",
        "    \"\"\"\n",
        "    nonzero_mask = create_nonzero_mask(data)\n",
        "    bbox = get_bbox_from_mask(nonzero_mask)\n",
        "    slicer = bounding_box_to_slice(bbox)\n",
        "    nonzero_mask = nonzero_mask[slicer][None]\n",
        "\n",
        "    slicer = (slice(None),) + slicer\n",
        "    data = data[slicer]\n",
        "    if seg is not None:\n",
        "        seg = seg[slicer]\n",
        "        seg[(seg == 0) & (~nonzero_mask)] = nonzero_label\n",
        "    else:\n",
        "        seg = np.where(nonzero_mask, np.int8(0), np.int8(nonzero_label))\n",
        "    return data, seg, bbox\n",
        "\n",
        "\n",
        "def compute_new_shape(old_shape: Union[Tuple[int, ...], List[int], np.ndarray],\n",
        "                      old_spacing: Union[Tuple[float, ...], List[float], np.ndarray],\n",
        "                      new_spacing: Union[Tuple[float, ...], List[float], np.ndarray]) -> np.ndarray:\n",
        "    assert len(old_spacing) == len(old_shape)\n",
        "    assert len(old_shape) == len(new_spacing)\n",
        "    new_shape = np.array([int(round(i / j * k)) for i, j, k in zip(old_spacing, new_spacing, old_shape)])\n",
        "    return new_shape\n",
        "\n",
        "\n",
        "def fast_resample_data_or_seg_to_shape(data: Union[torch.Tensor, np.ndarray],\n",
        "                                       new_shape: Union[Tuple[int, ...], List[int], np.ndarray],\n",
        "                                       current_spacing: Union[Tuple[float, ...], List[float], np.ndarray],\n",
        "                                       new_spacing: Union[Tuple[float, ...], List[float], np.ndarray],\n",
        "                                       is_seg: bool = False,\n",
        "                                       order: int = 3, order_z: int = 0,\n",
        "                                       force_separate_z: Union[bool, None] = False,\n",
        "                                       separate_z_anisotropy_threshold: float = ANISO_THRESHOLD):\n",
        "    use_gpu = True\n",
        "    device = torch.device(\"cuda\" if use_gpu else \"cpu\")\n",
        "    order_to_mode_map = {\n",
        "        0: \"nearest\",\n",
        "        1: \"trilinear\" if new_shape[0] > 1 else \"bilinear\",\n",
        "        2: \"trilinear\" if new_shape[0] > 1 else \"bilinear\",\n",
        "        3: \"trilinear\" if new_shape[0] > 1 else \"bicubic\",\n",
        "        4: \"trilinear\" if new_shape[0] > 1 else \"bicubic\",\n",
        "        5: \"trilinear\" if new_shape[0] > 1 else \"bicubic\",\n",
        "    }\n",
        "    resize_fn = torch.nn.functional.interpolate\n",
        "    kwargs = {\n",
        "        'mode': order_to_mode_map[order],\n",
        "        'align_corners': False\n",
        "    }\n",
        "    shape = np.array(data[0].shape)\n",
        "    new_shape = np.array(new_shape)\n",
        "    if np.any(shape != new_shape):\n",
        "        if not isinstance(data, torch.Tensor):\n",
        "            #torch_data = torch.from_numpy(data).float()\n",
        "            torch_data = torch.as_tensor(data.get())\n",
        "        else:\n",
        "            torch_data = data.float()\n",
        "        if new_shape[0] == 1:\n",
        "            torch_data = torch_data.transpose(1, 0)\n",
        "            new_shape = new_shape[1:]\n",
        "        else:\n",
        "            torch_data = torch_data.unsqueeze(0)\n",
        "\n",
        "        torch_data = resize_fn(torch_data.to(device), tuple(new_shape), **kwargs)\n",
        "\n",
        "        if new_shape[0] == 1:\n",
        "            torch_data = torch_data.transpose(1, 0)\n",
        "        else:\n",
        "            torch_data = torch_data.squeeze(0)\n",
        "\n",
        "        # if use_gpu:\n",
        "        #     torch_data = torch_data.cpu()\n",
        "        reshaped_final_data = torch_data\n",
        "        # if isinstance(data, np.ndarray):\n",
        "        #     reshaped_final_data = torch_data.numpy().astype(dtype_data)\n",
        "        # else:\n",
        "        #     reshaped_final_data = torch_data.to(dtype_data)\n",
        "\n",
        "        #print(f\"Reshaped data from {shape} to {new_shape}\")\n",
        "        #print(f\"reshaped_final_data shape: {reshaped_final_data.shape}\")\n",
        "        assert reshaped_final_data.ndim == 4, f\"reshaped_final_data.shape = {reshaped_final_data.shape}\"\n",
        "        return reshaped_final_data\n",
        "    else:\n",
        "        print(\"no resampling necessary\")\n",
        "        return data"
      ],
      "id": "f903ce2dc6a8c980"
    },
    {
      "metadata": {
        "id": "f71dbe0e5caa1e99"
      },
      "cell_type": "markdown",
      "source": [
        "## Upload Data"
      ],
      "id": "f71dbe0e5caa1e99"
    },
    {
      "metadata": {
        "id": "368d347daebbbe43",
        "outputId": "1fa4c285-de19-458c-a9bd-3f22146e984c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "cell_type": "code",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n",
            "rm: cannot remove 'inference_test_cases': No such file or directory\n",
            "Archive:  inference_test_cases.zip\n",
            " extracting: inference_test_cases/FLARETs_0001_0000.nii.gz  \n",
            " extracting: inference_test_cases/FLARETs_0015_0000.nii.gz  \n",
            " extracting: inference_test_cases/FLARETs_0018_0000.nii.gz  \n",
            " extracting: inference_test_cases/FLARETs_0036_0000.nii.gz  \n",
            " extracting: inference_test_cases/FLARETs_0046_0000.nii.gz  \n",
            "rm: cannot remove 'inference_test_logit': No such file or directory\n",
            "Archive:  inference_test_logit.zip\n",
            "  inflating: inference_test_logit/FLARETs_0001.pt  \n",
            "  inflating: inference_test_logit/FLARETs_0015.pt  \n",
            "  inflating: inference_test_logit/FLARETs_0018.pt  \n",
            "  inflating: inference_test_logit/FLARETs_0036.pt  \n",
            "  inflating: inference_test_logit/FLARETs_0046.pt  \n"
          ]
        }
      ],
      "execution_count": 2,
      "source": [
        "from google.colab import drive\n",
        "from os import mkdir\n",
        "from os.path import exists\n",
        "from shutil import copy\n",
        "\n",
        "drive.mount('/content/drive')\n",
        "!rm -r inference_test_cases\n",
        "mkdir(\"inference_test_cases\")\n",
        "if not exists(\"inference_test_cases.zip\"):\n",
        "    copy(\"/content/drive/MyDrive/inference_test_cases.zip\", \"inference_test_cases.zip\")\n",
        "!unzip inference_test_cases.zip -d inference_test_cases\n",
        "\n",
        "!rm -r inference_test_logit\n",
        "mkdir(\"inference_test_logit\")\n",
        "if not exists(\"inference_test_logit.zip\"):\n",
        "    copy(\"/content/drive/MyDrive/inference_test_logit.zip\", \"inference_test_logit.zip\")\n",
        "!unzip inference_test_logit.zip -d inference_test_logit"
      ],
      "id": "368d347daebbbe43"
    },
    {
      "cell_type": "code",
      "source": [
        "!rm inference_test_cases/FLARETs_0001_0000.nii.gz inference_test_logit/FLARETs_0001.pt"
      ],
      "metadata": {
        "id": "I1mO5JhgkNcH"
      },
      "id": "I1mO5JhgkNcH",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!rm inference_test_cases/FLARETs_0015_0000.nii.gz inference_test_logit/FLARETs_0015.pt"
      ],
      "metadata": {
        "id": "Wwm11xQ1yBuO"
      },
      "id": "Wwm11xQ1yBuO",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!rm inference_test_cases/FLARETs_0018_0000.nii.gz inference_test_logit/FLARETs_0018.pt"
      ],
      "metadata": {
        "id": "5INkxUj8yFpd"
      },
      "id": "5INkxUj8yFpd",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi"
      ],
      "metadata": {
        "id": "fD8egmfK5U-D"
      },
      "id": "fD8egmfK5U-D",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "id": "7dabdbac5071b697"
      },
      "cell_type": "markdown",
      "source": [
        "## Run Inference"
      ],
      "id": "7dabdbac5071b697"
    },
    {
      "metadata": {
        "id": "88ee2ea0f7d1e5a8",
        "outputId": "53ec2fbe-40c6-41ad-b573-ded1569d068b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "cell_type": "code",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Porcessing batch 0\n",
            "['FLARETs_0001_0000.nii.gz', 'FLARETs_0015_0000.nii.gz', 'FLARETs_0018_0000.nii.gz', 'FLARETs_0036_0000.nii.gz', 'FLARETs_0046_0000.nii.gz']\n",
            "FLARETs_0001_0000.nii.gz\n",
            "no resampling necessary\n",
            "FLARETs_0015_0000.nii.gz\n",
            "no resampling necessary\n",
            "FLARETs_0018_0000.nii.gz\n",
            "no resampling necessary\n",
            "FLARETs_0036_0000.nii.gz\n",
            "no resampling necessary\n",
            "FLARETs_0046_0000.nii.gz\n",
            "no resampling necessary\n",
            "Porcessing batch 1\n",
            "['FLARETs_0001_0000.nii.gz', 'FLARETs_0015_0000.nii.gz', 'FLARETs_0018_0000.nii.gz', 'FLARETs_0036_0000.nii.gz', 'FLARETs_0046_0000.nii.gz']\n",
            "FLARETs_0001_0000.nii.gz\n",
            "no resampling necessary\n",
            "FLARETs_0015_0000.nii.gz\n",
            "no resampling necessary\n",
            "FLARETs_0018_0000.nii.gz\n",
            "no resampling necessary\n",
            "FLARETs_0036_0000.nii.gz\n",
            "no resampling necessary\n",
            "FLARETs_0046_0000.nii.gz\n",
            "no resampling necessary\n",
            "Porcessing batch 2\n",
            "['FLARETs_0001_0000.nii.gz', 'FLARETs_0015_0000.nii.gz', 'FLARETs_0018_0000.nii.gz', 'FLARETs_0036_0000.nii.gz', 'FLARETs_0046_0000.nii.gz']\n",
            "FLARETs_0001_0000.nii.gz\n",
            "no resampling necessary\n",
            "FLARETs_0015_0000.nii.gz\n",
            "no resampling necessary\n",
            "FLARETs_0018_0000.nii.gz\n",
            "no resampling necessary\n",
            "FLARETs_0036_0000.nii.gz\n",
            "no resampling necessary\n",
            "FLARETs_0046_0000.nii.gz\n",
            "no resampling necessary\n",
            "Porcessing batch 3\n",
            "['FLARETs_0001_0000.nii.gz', 'FLARETs_0015_0000.nii.gz', 'FLARETs_0018_0000.nii.gz', 'FLARETs_0036_0000.nii.gz', 'FLARETs_0046_0000.nii.gz']\n",
            "FLARETs_0001_0000.nii.gz\n",
            "no resampling necessary\n",
            "FLARETs_0015_0000.nii.gz\n",
            "no resampling necessary\n",
            "FLARETs_0018_0000.nii.gz\n",
            "no resampling necessary\n",
            "FLARETs_0036_0000.nii.gz\n",
            "no resampling necessary\n",
            "FLARETs_0046_0000.nii.gz\n",
            "no resampling necessary\n",
            "Porcessing batch 4\n",
            "['FLARETs_0001_0000.nii.gz', 'FLARETs_0015_0000.nii.gz', 'FLARETs_0018_0000.nii.gz', 'FLARETs_0036_0000.nii.gz', 'FLARETs_0046_0000.nii.gz']\n",
            "FLARETs_0001_0000.nii.gz\n",
            "no resampling necessary\n",
            "FLARETs_0015_0000.nii.gz\n",
            "no resampling necessary\n",
            "FLARETs_0018_0000.nii.gz\n",
            "no resampling necessary\n",
            "FLARETs_0036_0000.nii.gz\n",
            "no resampling necessary\n",
            "FLARETs_0046_0000.nii.gz\n",
            "no resampling necessary\n",
            "Porcessing batch 5\n",
            "['FLARETs_0001_0000.nii.gz', 'FLARETs_0015_0000.nii.gz', 'FLARETs_0018_0000.nii.gz', 'FLARETs_0036_0000.nii.gz', 'FLARETs_0046_0000.nii.gz']\n",
            "FLARETs_0001_0000.nii.gz\n",
            "no resampling necessary\n",
            "FLARETs_0015_0000.nii.gz\n",
            "no resampling necessary\n",
            "FLARETs_0018_0000.nii.gz\n",
            "no resampling necessary\n",
            "FLARETs_0036_0000.nii.gz\n",
            "no resampling necessary\n",
            "FLARETs_0046_0000.nii.gz\n",
            "no resampling necessary\n",
            "Porcessing batch 6\n",
            "['FLARETs_0001_0000.nii.gz', 'FLARETs_0015_0000.nii.gz', 'FLARETs_0018_0000.nii.gz', 'FLARETs_0036_0000.nii.gz', 'FLARETs_0046_0000.nii.gz']\n",
            "FLARETs_0001_0000.nii.gz\n",
            "no resampling necessary\n",
            "FLARETs_0015_0000.nii.gz\n",
            "no resampling necessary\n",
            "FLARETs_0018_0000.nii.gz\n",
            "no resampling necessary\n",
            "FLARETs_0036_0000.nii.gz\n",
            "no resampling necessary\n",
            "FLARETs_0046_0000.nii.gz\n",
            "no resampling necessary\n",
            "Porcessing batch 7\n",
            "['FLARETs_0001_0000.nii.gz', 'FLARETs_0015_0000.nii.gz', 'FLARETs_0018_0000.nii.gz', 'FLARETs_0036_0000.nii.gz', 'FLARETs_0046_0000.nii.gz']\n",
            "FLARETs_0001_0000.nii.gz\n",
            "no resampling necessary\n",
            "FLARETs_0015_0000.nii.gz\n",
            "no resampling necessary\n",
            "FLARETs_0018_0000.nii.gz\n",
            "no resampling necessary\n",
            "FLARETs_0036_0000.nii.gz\n",
            "no resampling necessary\n",
            "FLARETs_0046_0000.nii.gz\n",
            "no resampling necessary\n",
            "Porcessing batch 8\n",
            "['FLARETs_0001_0000.nii.gz', 'FLARETs_0015_0000.nii.gz', 'FLARETs_0018_0000.nii.gz', 'FLARETs_0036_0000.nii.gz', 'FLARETs_0046_0000.nii.gz']\n",
            "FLARETs_0001_0000.nii.gz\n",
            "no resampling necessary\n",
            "FLARETs_0015_0000.nii.gz\n",
            "no resampling necessary\n",
            "FLARETs_0018_0000.nii.gz\n",
            "no resampling necessary\n",
            "FLARETs_0036_0000.nii.gz\n",
            "no resampling necessary\n",
            "FLARETs_0046_0000.nii.gz\n",
            "no resampling necessary\n",
            "Porcessing batch 9\n",
            "['FLARETs_0001_0000.nii.gz', 'FLARETs_0015_0000.nii.gz', 'FLARETs_0018_0000.nii.gz', 'FLARETs_0036_0000.nii.gz', 'FLARETs_0046_0000.nii.gz']\n",
            "FLARETs_0001_0000.nii.gz\n",
            "no resampling necessary\n",
            "FLARETs_0015_0000.nii.gz\n",
            "no resampling necessary\n",
            "FLARETs_0018_0000.nii.gz\n",
            "no resampling necessary\n",
            "FLARETs_0036_0000.nii.gz\n",
            "no resampling necessary\n",
            "FLARETs_0046_0000.nii.gz\n",
            "no resampling necessary\n",
            "{'resampling': [0.16939473152160645, 0.20920038223266602, 0.0838773250579834, 0.04087519645690918, 0.09788632392883301, 0.13160181045532227, 0.22777509689331055, 0.08383488655090332, 0.04085373878479004, 0.0910177230834961, 0.14214849472045898, 0.21056532859802246, 0.08388757705688477, 0.040801286697387695, 0.09168767929077148, 0.1420447826385498, 0.2061779499053955, 0.08320450782775879, 0.04078817367553711, 0.09577202796936035, 0.14224767684936523, 0.22274112701416016, 0.08384943008422852, 0.040662288665771484, 0.10167217254638672, 0.13165616989135742, 0.21106696128845215, 0.08380842208862305, 0.040810585021972656, 0.09120988845825195, 0.1409318447113037, 0.22305989265441895, 0.08385348320007324, 0.040841102600097656, 0.09789013862609863, 0.14223861694335938, 0.211564302444458, 0.0842740535736084, 0.040771484375, 0.09301137924194336, 0.13186931610107422, 0.23023653030395508, 0.08383417129516602, 0.04082179069519043, 0.09789919853210449, 0.13902592658996582, 0.20749402046203613, 0.08301973342895508, 0.040830135345458984, 0.09786510467529297], 'conversion': [0.16225290298461914, 0.10515546798706055, 0.03071141242980957, 0.009805679321289062, 0.06131601333618164, 0.08301925659179688, 0.1214604377746582, 0.030556440353393555, 0.009564399719238281, 0.06829214096069336, 0.09043598175048828, 0.1042637825012207, 0.030712127685546875, 0.009519815444946289, 0.06568241119384766, 0.09916424751281738, 0.10505819320678711, 0.02921891212463379, 0.00944209098815918, 0.07862401008605957, 0.10248613357543945, 0.10481667518615723, 0.03044867515563965, 0.009547233581542969, 0.08193421363830566, 0.08404064178466797, 0.10453557968139648, 0.030521392822265625, 0.009529829025268555, 0.06411552429199219, 0.09303402900695801, 0.10634827613830566, 0.028924942016601562, 0.009973287582397461, 0.08166146278381348, 0.11118769645690918, 0.10416030883789062, 0.030693531036376953, 0.00942540168762207, 0.07471442222595215, 0.09761595726013184, 0.10487222671508789, 0.0307309627532959, 0.009482383728027344, 0.0785360336303711, 0.0825810432434082, 0.10506606101989746, 0.028940916061401367, 0.009475469589233398, 0.06230521202087402]}\n",
            "[0.14131593704223633, 0.2159881591796875, 0.08374435901641845, 0.04080557823181152, 0.09559116363525391]\n",
            "[0.10058178901672363, 0.10657370090484619, 0.030145931243896484, 0.009576559066772461, 0.07171814441680908]\n"
          ]
        }
      ],
      "execution_count": 3,
      "source": [
        "from os import listdir\n",
        "from time import time\n",
        "from torch import load\n",
        "import gc\n",
        "from nnunetv2.imageio.simpleitk_reader_writer import SimpleITKIO\n",
        "\n",
        "\n",
        "def main() -> dict[str, list[float]]:\n",
        "    gc.collect()\n",
        "    torch.cuda.empty_cache()\n",
        "    cases = listdir(\"inference_test_cases\")\n",
        "    cases.sort()\n",
        "    print(cases)\n",
        "    results = {\"resampling\": [], \"conversion\": []}\n",
        "    for case in cases:\n",
        "        print(case)\n",
        "        image, properties = SimpleITKIO().read_images([f\"inference_test_cases/{case[:case.find('.')]}\"])\n",
        "        image = torch.from_numpy(image).to(dtype=torch.float32, memory_format=torch.contiguous_format).to('cuda')\n",
        "        data = image.clone()\n",
        "        data = data.permute([0, *[i + 1 for i in CT_configuration['transpose_forward']]])\n",
        "        shape_before_cropping = data.shape[1:]\n",
        "        properties['shape_before_cropping'] = shape_before_cropping\n",
        "        logit = load(f\"inference_test_logit/{case[:case.find('.')]}.pt\".replace(\"_0000\", \"\")).to(\"cuda\")\n",
        "        data, seg, bbox = crop_to_nonzero(data, None)\n",
        "        torch.cuda.synchronize()\n",
        "        properties['bbox_used_for_cropping'] = bbox\n",
        "        properties['shape_after_cropping_and_before_resampling'] = data.shape[1:]\n",
        "        spacing_transposed = [properties['spacing'][i] for i in CT_configuration['transpose_forward']]\n",
        "        current_spacing = CT_configuration['spacing'] if len(CT_configuration['spacing']) == len(\n",
        "            properties['shape_after_cropping_and_before_resampling']) else [spacing_transposed[0],\n",
        "                                                                            *CT_configuration['spacing']]\n",
        "        del image, data, seg, bbox\n",
        "        t0 = time()\n",
        "        predicted_logit = fast_resample_data_or_seg_to_shape(logit, properties['shape_after_cropping_and_before_resampling'],\n",
        "                                                        current_spacing, [properties['spacing'][i] for i in\n",
        "                                                                          CT_configuration['transpose_forward']])\n",
        "        torch.cuda.synchronize()\n",
        "        results[\"resampling\"].append(time() - t0)\n",
        "        t0 = time()\n",
        "        convert_predicted_logits_to_segmentation_with_correct_shape(predicted_logit, properties)\n",
        "        results[\"conversion\"].append(time() - t0)\n",
        "\n",
        "        gc.collect()\n",
        "        torch.cuda.empty_cache()\n",
        "        del logit, predicted_logit\n",
        "    return results\n",
        "\n",
        "\n",
        "def add_up(table: dict[str, list[float]], entry: str, n: int, num_cases: int) -> list[float]:\n",
        "    r = []\n",
        "    for i in range(num_cases):\n",
        "        s = 0\n",
        "        for j in range(n):\n",
        "            s += table[entry][i + j * num_cases]\n",
        "        r.append(s / n)\n",
        "    return r\n",
        "\n",
        "\n",
        "_final = {\"resampling\": [], \"conversion\": []}\n",
        "_n = 10\n",
        "_num_cases = 5\n",
        "for _b in range(_n):\n",
        "    print(f\"Porcessing batch {_b}\")\n",
        "    _r = main()\n",
        "    _final[\"resampling\"] += _r[\"resampling\"]\n",
        "    _final[\"conversion\"] += _r[\"conversion\"]\n",
        "print(_final)\n",
        "print(add_up(_final, \"resampling\", _n, _num_cases))\n",
        "print(add_up(_final, \"conversion\", _n, _num_cases))"
      ],
      "id": "88ee2ea0f7d1e5a8"
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 2
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython2",
      "version": "2.7.6"
    },
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 5
}