{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6286986e",
   "metadata": {},
   "source": [
    "Copyright (c) MONAI Consortium  \n",
    "Licensed under the Apache License, Version 2.0 (the \"License\");  \n",
    "you may not use this file except in compliance with the License.  \n",
    "You may obtain a copy of the License at  \n",
    "    http://www.apache.org/licenses/LICENSE-2.0  \n",
    "Unless required by applicable law or agreed to in writing, software  \n",
    "distributed under the License is distributed on an \"AS IS\" BASIS,  \n",
    "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  \n",
    "See the License for the specific language governing permissions and  \n",
    "limitations under the License.\n",
    "\n",
    "# MAISI Inference Tutorial\n",
    "\n",
    "This tutorial illustrates how to use trained MAISI model and codebase to generate synthetic 3D images and paired masks.\n",
    "\n",
    "`[Release Note (March 2025)]:` We are excited to announce the new MAISI Version `'maisi3d-rflow'`. Compared with the previous version `'maisi3d-ddpm'`, it accelerated latent diffusion model inference by 33x. Please see the detailed difference in the following section."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "301dab0b",
   "metadata": {},
   "source": [
    "## Setup environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f96b6f31",
   "metadata": {},
   "outputs": [],
   "source": [
    "!python -c \"import monai\" || pip install -q \"monai-weekly[nibabel, tqdm]\"\n",
    "!python -c \"import matplotlib\" || pip install -q matplotlib\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbc01d24",
   "metadata": {},
   "source": [
    "## Setup imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "67e2019e-1556-41a6-95e8-5d1a65f8b3a1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MONAI version: 1.6.dev2537\n",
      "Numpy version: 2.2.6\n",
      "Pytorch version: 2.8.0+cu128\n",
      "MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False\n",
      "MONAI rev id: ca9fa44c33f3b7c073263938e7a70b2fa6123044\n",
      "MONAI __file__: /home/<username>/miniconda3/envs/monai/lib/python3.10/site-packages/monai/__init__.py\n",
      "\n",
      "Optional dependencies:\n",
      "Pytorch Ignite version: NOT INSTALLED or UNKNOWN VERSION.\n",
      "ITK version: NOT INSTALLED or UNKNOWN VERSION.\n",
      "Nibabel version: NOT INSTALLED or UNKNOWN VERSION.\n",
      "scikit-image version: 0.25.2\n",
      "scipy version: 1.15.3\n",
      "Pillow version: 11.3.0\n",
      "Tensorboard version: NOT INSTALLED or UNKNOWN VERSION.\n",
      "gdown version: NOT INSTALLED or UNKNOWN VERSION.\n",
      "TorchVision version: NOT INSTALLED or UNKNOWN VERSION.\n",
      "tqdm version: 4.67.1\n",
      "lmdb version: NOT INSTALLED or UNKNOWN VERSION.\n",
      "psutil version: 5.9.1\n",
      "pandas version: NOT INSTALLED or UNKNOWN VERSION.\n",
      "einops version: NOT INSTALLED or UNKNOWN VERSION.\n",
      "transformers version: NOT INSTALLED or UNKNOWN VERSION.\n",
      "mlflow version: NOT INSTALLED or UNKNOWN VERSION.\n",
      "pynrrd version: NOT INSTALLED or UNKNOWN VERSION.\n",
      "clearml version: NOT INSTALLED or UNKNOWN VERSION.\n",
      "\n",
      "For details about installing the optional dependencies, please visit:\n",
      "    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import argparse\n",
    "import json\n",
    "import os\n",
    "import tempfile\n",
    "\n",
    "import monai\n",
    "import torch\n",
    "from monai.apps import download_url\n",
    "from monai.config import print_config\n",
    "from monai.transforms import LoadImage, Orientation\n",
    "from monai.utils import set_determinism\n",
    "from scripts.sample import LDMSampler, check_input\n",
    "from scripts.utils import define_instance\n",
    "from scripts.utils_plot import find_label_center_loc, get_xyz_plot, show_image\n",
    "from scripts.diff_model_setting import setup_logging\n",
    "\n",
    "print_config()\n",
    "\n",
    "logger = setup_logging(\"notebook\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37c2759e-d1fa-42d7-b208-fbe306ac1e06",
   "metadata": {},
   "source": [
    "## Set up the MAISI version\n",
    "\n",
    "Choose between `'maisi3d-ddpm'` and `'maisi3d-rflow'`. The differences are:\n",
    "- The maisi version `'maisi3d-ddpm'` uses basic noise scheduler DDPM. `'maisi3d-rflow'` uses Rectified Flow scheduler, can be 33 times faster during inference.\n",
    "- The maisi version `'maisi3d-ddpm'` requires training images to be labeled with body region (`\"top_region_index\"` and `\"bottom_region_index\"`), while `'maisi3d-rflow'` does not have such requirement. In other words, it is easier to prepare training data for `'maisi3d-rflow'`.\n",
    "- For the released model weights, `'maisi3d-rflow'` can generate images with better quality for head region and small output volumes, and comparable quality for other cases compared with `'maisi3d-ddpm'`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bf4252b1-089d-48c1-b6d6-aa24a93a5839",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2025-09-19 02:27:49.124][ INFO](notebook) - MAISI version is maisi3d-rflow, whether to use body_region is False\n"
     ]
    }
   ],
   "source": [
    "maisi_version = \"maisi3d-rflow\"\n",
    "if maisi_version == \"maisi3d-ddpm\":\n",
    "    model_def_path = \"./configs/config_maisi3d-ddpm.json\"\n",
    "elif maisi_version == \"maisi3d-rflow\":\n",
    "    model_def_path = \"./configs/config_maisi3d-rflow.json\"\n",
    "else:\n",
    "    raise ValueError(f\"maisi_version has to be chosen from ['maisi3d-ddpm', 'maisi3d-rflow'], yet got {maisi_version}.\")\n",
    "with open(model_def_path, \"r\") as f:\n",
    "    model_def = json.load(f)\n",
    "include_body_region = model_def[\"include_body_region\"]\n",
    "logger.info(f\"MAISI version is {maisi_version}, whether to use body_region is {include_body_region}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50e37a43",
   "metadata": {},
   "source": [
    "## Setup data directory\n",
    "You can specify a directory with the `MONAI_DATA_DIRECTORY` environment variable. This allows you to save results and reuse downloads. If not specified a temporary directory will be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3c12dcc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/shuhan/miniconda3/envs/monai/lib/python3.10/site-packages/monai/apps/utils.py:118: UserWarning: tqdm is not installed, will not show the downloading progress bar.\n",
      "  warnings.warn(\"tqdm is not installed, will not show the downloading progress bar.\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2025-09-19 02:28:43,890 - INFO - Downloaded: models/autoencoder_epoch273.pt\n",
      "2025-09-19 02:28:43,892 - INFO - Expected md5 is None, skip md5 check for file models/autoencoder_epoch273.pt.\n",
      "2025-09-19 02:28:51,082 - INFO - Downloaded: models/mask_generation_autoencoder.pt\n",
      "2025-09-19 02:28:51,083 - INFO - Expected md5 is None, skip md5 check for file models/mask_generation_autoencoder.pt.\n",
      "2025-09-19 02:34:07,581 - INFO - Downloaded: models/mask_generation_diffusion_unet.pt\n",
      "2025-09-19 02:34:07,586 - INFO - Expected md5 is None, skip md5 check for file models/mask_generation_diffusion_unet.pt.\n",
      "2025-09-19 02:34:16,963 - INFO - Downloaded: configs/all_anatomy_size_condtions.json\n",
      "2025-09-19 02:34:16,964 - INFO - Expected md5 is None, skip md5 check for file configs/all_anatomy_size_condtions.json.\n"
     ]
    }
   ],
   "source": [
    "os.environ[\"MONAI_DATA_DIRECTORY\"] = \"temp_work_dir_inference_demo\"\n",
    "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n",
    "if directory is not None:\n",
    "    os.makedirs(directory, exist_ok=True)\n",
    "root_dir = tempfile.mkdtemp() if directory is None else directory\n",
    "\n",
    "# TODO: remove the `files` after the files are uploaded to the NGC\n",
    "files = [\n",
    "    {\n",
    "        \"path\": \"models/autoencoder_epoch273.pt\",\n",
    "        \"url\": \"https://developer.download.nvidia.com/assets/Clara/monai/tutorials\"\n",
    "        \"/model_zoo/model_maisi_autoencoder_epoch273_alternative.pt\",\n",
    "    },\n",
    "    {\n",
    "        \"path\": \"models/mask_generation_autoencoder.pt\",\n",
    "        \"url\": \"https://developer.download.nvidia.com/assets/Clara/monai\" \"/tutorials/mask_generation_autoencoder.pt\",\n",
    "    },\n",
    "    {\n",
    "        \"path\": \"models/mask_generation_diffusion_unet.pt\",\n",
    "        \"url\": \"https://developer.download.nvidia.com/assets/Clara/monai\"\n",
    "        \"/tutorials/model_zoo/model_maisi_mask_generation_diffusion_unet_v2.pt\",\n",
    "    },\n",
    "    {\n",
    "        \"path\": \"configs/all_anatomy_size_condtions.json\",\n",
    "        \"url\": \"https://developer.download.nvidia.com/assets/Clara/monai/tutorials/all_anatomy_size_condtions.json\",\n",
    "    },\n",
    "    {\n",
    "        \"path\": \"datasets/all_masks_flexible_size_and_spacing_4000.zip\",\n",
    "        \"url\": \"https://developer.download.nvidia.com/assets/Clara/monai\"\n",
    "        \"/tutorials/all_masks_flexible_size_and_spacing_4000.zip\",\n",
    "    },\n",
    "]\n",
    "\n",
    "if maisi_version == \"maisi3d-ddpm\":\n",
    "    files += [\n",
    "        {\n",
    "            \"path\": \"models/diff_unet_3d_ddpm.pt\",\n",
    "            \"url\": \"https://developer.download.nvidia.com/assets/Clara/monai/tutorials/model_zoo\"\n",
    "            \"/model_maisi_input_unet3d_data-all_steps1000size512ddpm_random_current_inputx_v1_alternative.pt\",\n",
    "        },\n",
    "        {\n",
    "            \"path\": \"models/controlnet_3d_ddpm.pt\",\n",
    "            \"url\": \"https://developer.download.nvidia.com/assets/Clara/monai/tutorials/model_zoo\"\n",
    "            \"/model_maisi_controlnet-20datasets-e20wl100fold0bc_noi_dia_fsize_current_alternative.pt\",\n",
    "        },\n",
    "        {\n",
    "            \"path\": \"configs/candidate_masks_flexible_size_and_spacing_3000.json\",\n",
    "            \"url\": \"https://developer.download.nvidia.com/assets/Clara/monai\"\n",
    "            \"/tutorials/candidate_masks_flexible_size_and_spacing_3000.json\",\n",
    "        },\n",
    "    ]\n",
    "elif maisi_version == \"maisi3d-rflow\":\n",
    "    files += [\n",
    "        {\n",
    "            \"path\": \"models/diff_unet_3d_rflow.pt\",\n",
    "            \"url\": \"https://developer.download.nvidia.com/assets/Clara/monai/tutorials/\"\n",
    "            \"diff_unet_ckpt_rflow_epoch19350.pt\",\n",
    "        },\n",
    "        {\n",
    "            \"path\": \"models/controlnet_3d_rflow.pt\",\n",
    "            \"url\": \"https://developer.download.nvidia.com/assets/Clara/monai/tutorials/controlnet_rflow_epoch60.pt\",\n",
    "        },\n",
    "        {\n",
    "            \"path\": \"configs/candidate_masks_flexible_size_and_spacing_4000.json\",\n",
    "            \"url\": \"https://developer.download.nvidia.com/assets/Clara/monai\"\n",
    "            \"/tutorials/candidate_masks_flexible_size_and_spacing_4000.json\",\n",
    "        },\n",
    "    ]\n",
    "else:\n",
    "    raise ValueError(f\"maisi_version has to be chosen from ['maisi3d-ddpm', 'maisi3d-rflow'], yet got {maisi_version}.\")\n",
    "\n",
    "for file in files:\n",
    "    file[\"path\"] = file[\"path\"] if \"datasets/\" not in file[\"path\"] else os.path.join(root_dir, file[\"path\"])\n",
    "    download_url(url=file[\"url\"], filepath=file[\"path\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f164998-6c7d-44cd-93ee-e9d36c26ef96",
   "metadata": {},
   "source": [
    "## Read in environment setting, including data directory, model directory, and output directory\n",
    "\n",
    "The information for data directory, model directory, and output directory are saved in ./configs/environment.json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c38b4c33",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2025-03-19 05:06:22.493][ INFO](notebook) - output_dir: output\n",
      "[2025-03-19 05:06:22.494][ INFO](notebook) - trained_autoencoder_path: models/autoencoder_epoch273.pt\n",
      "[2025-03-19 05:06:22.494][ INFO](notebook) - trained_diffusion_path: models/diff_unet_3d_rflow.pt\n",
      "[2025-03-19 05:06:22.495][ INFO](notebook) - trained_controlnet_path: models/controlnet_3d_rflow.pt\n",
      "[2025-03-19 05:06:22.496][ INFO](notebook) - trained_mask_generation_autoencoder_path: models/mask_generation_autoencoder.pt\n",
      "[2025-03-19 05:06:22.497][ INFO](notebook) - trained_mask_generation_diffusion_path: models/mask_generation_diffusion_unet.pt\n",
      "[2025-03-19 05:06:22.497][ INFO](notebook) - all_mask_files_base_dir: temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000\n",
      "[2025-03-19 05:06:22.498][ INFO](notebook) - all_mask_files_json: ./configs/candidate_masks_flexible_size_and_spacing_4000.json\n",
      "[2025-03-19 05:06:22.498][ INFO](notebook) - all_anatomy_size_conditions_json: ./configs/all_anatomy_size_condtions.json\n",
      "[2025-03-19 05:06:22.499][ INFO](notebook) - label_dict_json: ./configs/label_dict.json\n",
      "[2025-03-19 05:06:22.500][ INFO](notebook) - label_dict_remap_json: ./configs/label_dict_124_to_132.json\n",
      "[2025-03-19 05:06:22.501][ INFO](notebook) - Global config variables have been loaded.\n"
     ]
    }
   ],
   "source": [
    "args = argparse.Namespace()\n",
    "\n",
    "if maisi_version == \"maisi3d-ddpm\":\n",
    "    environment_file = \"./configs/environment_maisi3d-ddpm.json\"\n",
    "elif maisi_version == \"maisi3d-rflow\":\n",
    "    environment_file = \"./configs/environment_maisi3d-rflow.json\"\n",
    "else:\n",
    "    raise ValueError(f\"maisi_version has to be chosen from ['maisi3d-ddpm', 'maisi3d-rflow'], yet got {maisi_version}.\")\n",
    "\n",
    "with open(environment_file, \"r\") as f:\n",
    "    env_dict = json.load(f)\n",
    "for k, v in env_dict.items():\n",
    "    # Update the path to the downloaded dataset in MONAI_DATA_DIRECTORY\n",
    "    val = v if \"datasets/\" not in v else os.path.join(root_dir, v)\n",
    "    setattr(args, k, val)\n",
    "    logger.info(f\"{k}: {val}\")\n",
    "logger.info(\"Global config variables have been loaded.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae98233f-6492-40ed-9ba5-7ab4ae0f8ffd",
   "metadata": {},
   "source": [
    "## Read in configuration setting, including network definition, body region and anatomy to generate, etc.\n",
    "\n",
    "The information for the inference input, like body region and anatomy to generate, is stored in \"./configs/config_infer.json\". Please refer to README.md for the details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "533414f3-bef5-49f7-b082-f803b5e494bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2025-03-19 05:06:22.508][ INFO](notebook) - num_output_samples: 1\n",
      "[2025-03-19 05:06:22.509][ INFO](notebook) - body_region: ['chest']\n",
      "[2025-03-19 05:06:22.509][ INFO](notebook) - anatomy_list: ['lung tumor']\n",
      "[2025-03-19 05:06:22.510][ INFO](notebook) - controllable_anatomy_size: []\n",
      "[2025-03-19 05:06:22.510][ INFO](notebook) - num_inference_steps: 30\n",
      "[2025-03-19 05:06:22.512][ INFO](notebook) - mask_generation_num_inference_steps: 1000\n",
      "[2025-03-19 05:06:22.512][ INFO](notebook) - output_size: [256, 256, 256]\n",
      "[2025-03-19 05:06:22.513][ INFO](notebook) - image_output_ext: .nii.gz\n",
      "[2025-03-19 05:06:22.514][ INFO](notebook) - label_output_ext: .nii.gz\n",
      "[2025-03-19 05:06:22.514][ INFO](notebook) - spacing: [1.5, 1.5, 2.0]\n",
      "[2025-03-19 05:06:22.515][ INFO](notebook) - autoencoder_sliding_window_infer_size: [48, 48, 48]\n",
      "[2025-03-19 05:06:22.515][ INFO](notebook) - autoencoder_sliding_window_infer_overlap: 0.6666\n",
      "[2025-03-19 05:06:22.516][ INFO](notebook) - controlnet: $@controlnet_def\n",
      "[2025-03-19 05:06:22.517][ INFO](notebook) - diffusion_unet: $@diffusion_unet_def\n",
      "[2025-03-19 05:06:22.518][ INFO](notebook) - autoencoder: $@autoencoder_def\n",
      "[2025-03-19 05:06:22.518][ INFO](notebook) - mask_generation_autoencoder: $@mask_generation_autoencoder_def\n",
      "[2025-03-19 05:06:22.519][ INFO](notebook) - mask_generation_diffusion: $@mask_generation_diffusion_def\n",
      "[2025-03-19 05:06:22.520][ INFO](notebook) - modality: 1\n",
      "[2025-03-19 05:06:22.521][ INFO](root) - `controllable_anatomy_size` is empty.\n",
      "We will synthesize based on `body_region`: (['chest']) and `anatomy_list`: (['lung tumor']).\n",
      "[2025-03-19 05:06:22.522][ INFO](root) - The generate results will have voxel size to be [1.5, 1.5, 2.0]mm, volume size to be [256, 256, 256].\n",
      "[2025-03-19 05:06:22.522][ INFO](notebook) - Network definition and inference inputs have been loaded.\n"
     ]
    }
   ],
   "source": [
    "with open(model_def_path, \"r\") as f:\n",
    "    model_def = json.load(f)\n",
    "for k, v in model_def.items():\n",
    "    setattr(args, k, v)\n",
    "\n",
    "# check the format of inference inputs\n",
    "config_infer_file = \"./configs/config_infer.json\"\n",
    "with open(config_infer_file, \"r\") as f:\n",
    "    config_infer_dict = json.load(f)\n",
    "for k, v in config_infer_dict.items():\n",
    "    setattr(args, k, v)\n",
    "    logger.info(f\"{k}: {v}\")\n",
    "\n",
    "check_input(\n",
    "    args.body_region,\n",
    "    args.anatomy_list,\n",
    "    args.label_dict_json,\n",
    "    args.output_size,\n",
    "    args.spacing,\n",
    "    args.controllable_anatomy_size,\n",
    ")\n",
    "latent_shape = [args.latent_channels, args.output_size[0] // 4, args.output_size[1] // 4, args.output_size[2] // 4]\n",
    "logger.info(\"Network definition and inference inputs have been loaded.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d22296e5",
   "metadata": {},
   "source": [
    "## Set deterministic training for reproducibility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "87ba613d-a2f5-4afc-95df-65ad21fafedd",
   "metadata": {},
   "outputs": [],
   "source": [
    "set_determinism(seed=0)\n",
    "args.random_seed = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02476bc7-c980-4b0b-8d7a-c780a5ccc11f",
   "metadata": {},
   "source": [
    "## Initialize networks and noise scheduler, then load the trained model weights.\n",
    "\n",
    "The networks and noise scheduler are defined in `config_maisi.json`. We will read them in and load the model weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d499f7b1",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2025-03-19 05:06:28,553 - INFO - 'dst' model updated: 180 of 231 variables.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2025-03-19 05:06:30.944][ INFO](notebook) - All the trained model weights have been loaded.\n"
     ]
    }
   ],
   "source": [
    "noise_scheduler = define_instance(args, \"noise_scheduler\")\n",
    "mask_generation_noise_scheduler = define_instance(args, \"mask_generation_noise_scheduler\")\n",
    "\n",
    "device = torch.device(\"cuda\")\n",
    "\n",
    "autoencoder = define_instance(args, \"autoencoder_def\").to(device)\n",
    "checkpoint_autoencoder = torch.load(args.trained_autoencoder_path, weights_only=True)\n",
    "autoencoder.load_state_dict(checkpoint_autoencoder)\n",
    "\n",
    "diffusion_unet = define_instance(args, \"diffusion_unet_def\").to(device)\n",
    "checkpoint_diffusion_unet = torch.load(args.trained_diffusion_path, weights_only=False)\n",
    "diffusion_unet.load_state_dict(checkpoint_diffusion_unet[\"unet_state_dict\"], strict=True)\n",
    "scale_factor = checkpoint_diffusion_unet[\"scale_factor\"].to(device)\n",
    "\n",
    "controlnet = define_instance(args, \"controlnet_def\").to(device)\n",
    "checkpoint_controlnet = torch.load(args.trained_controlnet_path, weights_only=False)\n",
    "monai.networks.utils.copy_model_state(controlnet, diffusion_unet.state_dict())\n",
    "controlnet.load_state_dict(checkpoint_controlnet[\"controlnet_state_dict\"], strict=True)\n",
    "\n",
    "mask_generation_autoencoder = define_instance(args, \"mask_generation_autoencoder_def\").to(device)\n",
    "checkpoint_mask_generation_autoencoder = torch.load(args.trained_mask_generation_autoencoder_path, weights_only=True)\n",
    "mask_generation_autoencoder.load_state_dict(checkpoint_mask_generation_autoencoder)\n",
    "\n",
    "mask_generation_diffusion_unet = define_instance(args, \"mask_generation_diffusion_def\").to(device)\n",
    "checkpoint_mask_generation_diffusion_unet = torch.load(args.trained_mask_generation_diffusion_path, weights_only=True)\n",
    "mask_generation_diffusion_unet.load_state_dict(checkpoint_mask_generation_diffusion_unet[\"unet_state_dict\"])\n",
    "mask_generation_scale_factor = checkpoint_mask_generation_diffusion_unet[\"scale_factor\"]\n",
    "\n",
    "logger.info(\"All the trained model weights have been loaded.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9125f7c8",
   "metadata": {},
   "source": [
    "## Define the LDM Sampler, which contains functions that will perform the inference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8685da6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2025-03-19 05:06:30.969][ INFO](root) - LDM sampler initialized.\n"
     ]
    }
   ],
   "source": [
    "ldm_sampler = LDMSampler(\n",
    "    args.body_region,\n",
    "    args.anatomy_list,\n",
    "    args.all_mask_files_json,\n",
    "    args.all_anatomy_size_conditions_json,\n",
    "    args.all_mask_files_base_dir,\n",
    "    args.label_dict_json,\n",
    "    args.label_dict_remap_json,\n",
    "    autoencoder,\n",
    "    diffusion_unet,\n",
    "    controlnet,\n",
    "    noise_scheduler,\n",
    "    scale_factor,\n",
    "    mask_generation_autoencoder,\n",
    "    mask_generation_diffusion_unet,\n",
    "    mask_generation_scale_factor,\n",
    "    mask_generation_noise_scheduler,\n",
    "    device,\n",
    "    latent_shape,\n",
    "    args.mask_generation_latent_shape,\n",
    "    args.output_size,\n",
    "    args.output_dir,\n",
    "    args.controllable_anatomy_size,\n",
    "    image_output_ext=args.image_output_ext,\n",
    "    label_output_ext=args.label_output_ext,\n",
    "    spacing=args.spacing,\n",
    "    modality=args.modality,\n",
    "    num_inference_steps=args.num_inference_steps,\n",
    "    mask_generation_num_inference_steps=args.mask_generation_num_inference_steps,\n",
    "    random_seed=args.random_seed,\n",
    "    autoencoder_sliding_window_infer_size=args.autoencoder_sliding_window_infer_size,\n",
    "    autoencoder_sliding_window_infer_overlap=args.autoencoder_sliding_window_infer_overlap,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af0a7fce-e9ae-48a0-b74a-47f880369efa",
   "metadata": {},
   "source": [
    "## Perform the inference\n",
    "**Time cost:** It will take around 80s to generate a [256,256,256] mask, and another 105s to generate the corresponding [256,256,256] image on one A100. The time cost per mask is fixed; time cost per image is roughly linear to the output size. i.e., if we want to generate a [512,512,512] image/mask pair, it will take around 80s to generate a [256,256,256] mask and then resample it to a [512,512,512] mask. The time cost for [512,512,512] image generatation will be 8 times longer, around 8x105s~14min. \n",
    "\n",
    "**GPU memory:** Decoding the latents into image with autoencoder is the step that consumes the largest GPU memory. The mask generation takes 34G. For image generation, to save GPU memory, we use sliding window inference for the autoencoder when the `output_size` is large. So the GPU memory for image generation is not strictly linear to the output_size. For [256,256,256], it takes 34G. For [512,512,512], it takes 67G."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "271f91bf-1c55-46e2-ae56-8677cd8eb81f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2025-03-19 05:06:30.974][ INFO](notebook) - The generated image/mask pairs will be saved in output.\n",
      "[2025-03-19 05:06:31.018][ INFO](root) - Resample mask file to get desired output size and spacing\n",
      "[2025-03-19 05:06:32.950][ INFO](root) - Resampling mask to target shape and spacing\n",
      "[2025-03-19 05:06:32.953][ INFO](root) - Resize Spacing: [tensor(0.7988, dtype=torch.float64), tensor(0.7988, dtype=torch.float64), tensor(1.1016, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n",
      "[2025-03-19 05:06:32.954][ INFO](root) - Output size: [512, 512, 384] -> [256, 256, 256]\n",
      "[2025-03-19 05:06:35.876][ INFO](root) - Resampling mask to target shape and spacing\n",
      "[2025-03-19 05:06:35.878][ INFO](root) - Resize Spacing: [tensor(0.7031, dtype=torch.float64), tensor(0.7031, dtype=torch.float64), tensor(1.4795, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n",
      "[2025-03-19 05:06:35.879][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n",
      "[2025-03-19 05:06:38.564][ INFO](root) - Resampling mask to target shape and spacing\n",
      "[2025-03-19 05:06:38.566][ INFO](root) - Resize Spacing: [tensor(0.7617, dtype=torch.float64), tensor(0.7617, dtype=torch.float64), tensor(1.2939, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n",
      "[2025-03-19 05:06:38.567][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n",
      "[2025-03-19 05:06:41.321][ INFO](root) - Resampling mask to target shape and spacing\n",
      "[2025-03-19 05:06:41.324][ INFO](root) - Resize Spacing: [tensor(0.7031, dtype=torch.float64), tensor(0.7031, dtype=torch.float64), tensor(1.4209, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n",
      "[2025-03-19 05:06:41.325][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n",
      "[2025-03-19 05:06:46.041][ INFO](root) - Resampling mask to target shape and spacing\n",
      "[2025-03-19 05:06:46.043][ INFO](root) - Resize Spacing: [tensor(0.7422, dtype=torch.float64), tensor(0.7422, dtype=torch.float64), tensor(0.5752, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n",
      "[2025-03-19 05:06:46.044][ INFO](root) - Output size: [512, 512, 640] -> [256, 256, 256]\n",
      "[2025-03-19 05:06:47.854][ INFO](root) - Images will be generated based on [{'mask_file': {'pseudo_label': 'temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000/./Task06/labelsTr/lung_070_133combined_aug_wbdm.nii.gz', 'spacing': [1.5, 1.5, 2.0], 'dim': [256, 256, 256], 'top_region_index': [1, 0, 0, 0], 'bottom_region_index': [0, 0, 1, 0]}, 'if_aug': True}, {'mask_file': {'pseudo_label': 'temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000/./Task06/labelsTr/lung_031_133combined_aug_wbdm.nii.gz', 'spacing': [1.5, 1.5, 2.0], 'dim': [256, 256, 256], 'top_region_index': [0, 1, 0, 0], 'bottom_region_index': [0, 0, 1, 0]}, 'if_aug': True}, {'mask_file': {'pseudo_label': 'temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000/./Task06/labelsTr/lung_075_133combined_aug_wbdm.nii.gz', 'spacing': [1.5, 1.5, 2.0], 'dim': [256, 256, 256], 'top_region_index': [1, 0, 0, 0], 'bottom_region_index': [0, 0, 1, 0]}, 'if_aug': True}, {'mask_file': {'pseudo_label': 'temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000/./Task06/labelsTr/lung_014_133combined_aug_wbdm.nii.gz', 'spacing': [1.5, 1.5, 2.0], 'dim': [256, 256, 256], 'top_region_index': [0, 1, 0, 0], 'bottom_region_index': [0, 0, 1, 0]}, 'if_aug': True}, {'mask_file': {'pseudo_label': 'temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000/./Task06/labelsTr/lung_049_133combined_aug_wbdm.nii.gz', 'spacing': [1.5, 1.5, 2.0], 'dim': [256, 256, 256], 'top_region_index': [1, 0, 0, 0], 'bottom_region_index': [0, 0, 1, 0]}, 'if_aug': True}].\n",
      "[2025-03-19 05:06:47.855][ INFO](root) - ---- Start preparing masks... ----\n",
      "[2025-03-19 05:06:49.096][ INFO](root) - Resampling mask to target shape and spacing\n",
      "[2025-03-19 05:06:49.100][ INFO](root) - Resize Spacing: [tensor(0.7617, dtype=torch.float64), tensor(0.7617, dtype=torch.float64), tensor(1.2939, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n",
      "[2025-03-19 05:06:49.100][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "augmenting lung tumor\n",
      "28\n",
      "metatensor(180., device='cuda:0') | metatensor(547.4000, device='cuda:0')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2025-03-19 05:06:52.345][ INFO](root) - ---- Mask preparation time: 4.489694595336914 seconds ----\n",
      "[2025-03-19 05:06:52.363][ INFO](root) - ---- Start generating latent features... ----\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "metatensor(687., device='cuda:0') | metatensor(547.4000, device='cuda:0')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 30/30 [00:06<00:00,  4.58it/s]\n",
      "[2025-03-19 05:06:58.962][ INFO](root) - ---- DM/ControlNet Latent features generation time: 6.598896265029907 seconds ----\n",
      "[2025-03-19 05:06:59.051][ INFO](root) - ---- Start decoding latent features into images... ----\n",
      "100%|█████████████████████████████████████████████| 8/8 [00:11<00:00,  1.42s/it]\n",
      "[2025-03-19 05:07:10.494][ INFO](root) - ---- Image VAE decoding time: 11.442715883255005 seconds ----\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 5\n",
      "2025-03-19 05:07:10,994 INFO image_writer.py:197 - writing: output/sample_20250319_050710_975233_image.nii.gz\n",
      "2025-03-19 05:07:12,461 INFO image_writer.py:197 - writing: output/sample_20250319_050710_975233_label.nii.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2025-03-19 05:07:13.422][ INFO](notebook) - MAISI image/mask generation finished\n"
     ]
    }
   ],
   "source": [
    "logger.info(f\"The generated image/mask pairs will be saved in {args.output_dir}.\")\n",
    "output_filenames = ldm_sampler.sample_multiple_images(args.num_output_samples)\n",
    "logger.info(\"MAISI image/mask generation finished\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "114bb8d7-17db-48b5-8d08-d8af61fc763a",
   "metadata": {},
   "source": [
    "## Visualize the results\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dfd2ebf9-04f9-498f-982e-9daf44602bee",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2025-03-19 05:07:13.430][ INFO](notebook) - Visualizing output/sample_20250319_050710_975233_image.nii.gz and output/sample_20250319_050710_975233_label.nii.gz...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2400x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2400x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_image_filename = output_filenames[0][0]\n",
    "visualize_mask_filename = output_filenames[0][1]\n",
    "logger.info(f\"Visualizing {visualize_image_filename} and {visualize_mask_filename}...\")\n",
    "\n",
    "# load image/mask pairs\n",
    "loader = LoadImage(image_only=True, ensure_channel_first=True)\n",
    "orientation = Orientation(axcodes=\"RAS\")\n",
    "image_volume = orientation(loader(visualize_image_filename))\n",
    "mask_volume = orientation(loader(visualize_mask_filename)).to(torch.uint8)\n",
    "\n",
    "# visualize for CT HU intensity between [-200, 500]\n",
    "image_volume = torch.clip(image_volume, -1000, 300)\n",
    "image_volume = image_volume - torch.min(image_volume)\n",
    "image_volume = image_volume / torch.max(image_volume)\n",
    "\n",
    "# create a random color map for mask visualization\n",
    "colorize = torch.clip(torch.cat([torch.zeros(3, 1, 1, 1), torch.randn(3, 200, 1, 1)], 1), 0, 1)\n",
    "target_class_index = 1\n",
    "\n",
    "# find center voxel location for 2D slice visualization\n",
    "center_loc_axis = find_label_center_loc(torch.flip(mask_volume[0, ...] == target_class_index, [-3, -2, -1]))\n",
    "\n",
    "# visualization\n",
    "vis_mask = get_xyz_plot(\n",
    "    mask_volume, center_loc_axis, mask_bool=True, n_label=201, colorize=colorize, target_class_index=target_class_index\n",
    ")\n",
    "show_image(vis_mask, title=\"mask\")\n",
    "\n",
    "vis_image = get_xyz_plot(image_volume, center_loc_axis, mask_bool=False)\n",
    "show_image(vis_image, title=\"image\")"
   ]
  }
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