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SimoneBendazzoli93SimoneBendazzoli93
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Enhance nnUNet MONAI Bundle Notebook with data handling and configuration updates
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‎bundle/06_nnunet_monai_bundle.ipynb

Lines changed: 110 additions & 22 deletions
Original file line numberDiff line numberDiff line change
@@ -69,8 +69,10 @@
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"import tempfile\n",
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"from monai.bundle.config_parser import ConfigParser\n",
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"from monai.apps.nnunet import nnUNetV2Runner\n",
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"\n",
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"import random\n",
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"from monai.bundle.nnunet import convert_nnunet_to_monai_bundle\n",
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"import json\n",
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"from pathlib import Path\n",
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"\n",
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"print_config()"
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]
@@ -86,6 +88,15 @@
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"If not specified a temporary directory will be used."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"os.environ[\"MONAI_DATA_DIRECTORY\"] = \"/home/maia-user/Documents/GitHub/tutorials/bundle/MONAI/Data\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
@@ -109,10 +120,84 @@
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]
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},
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{
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"cell_type": "markdown",
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"dataroot = os.path.join(root_dir, \"Task09_Spleen/\")\n",
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"\n",
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"test_dir = os.path.join(dataroot, \"imagesTs/\")\n",
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"train_dir = os.path.join(dataroot, \"imagesTr/\")\n",
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"label_dir = os.path.join(dataroot, \"labelsTr/\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"datalist_json = {\"testing\": [], \"training\": []}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"datalist_json[\"testing\"] = [\n",
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" {\"image\": \"./imagesTs/\" + file} for file in os.listdir(test_dir) if (\".nii.gz\" in file) and (\"._\" not in file)\n",
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"]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"datalist_json[\"training\"] = [\n",
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" {\"image\": \"./imagesTr/\" + file, \"label\": \"./labelsTr/\" + file, \"fold\": 0}\n",
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" for file in os.listdir(train_dir)\n",
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" if (\".nii.gz\" in file) and (\"._\" not in file)\n",
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"] # Initialize as single fold"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"random.seed(42)\n",
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"random.shuffle(datalist_json[\"training\"])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"num_folds = 5\n",
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"fold_size = len(datalist_json[\"training\"]) // num_folds\n",
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"for i in range(num_folds):\n",
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" for j in range(fold_size):\n",
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" datalist_json[\"training\"][i * fold_size + j][\"fold\"] = i"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"At the end of the notebook, remember to copy the generated `msd_task09_spleen_folds.json` file to the `<root_dir>/Task09_Spleen` directory."
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"datalist_file = Path(root_dir).joinpath(\"Task09_Spleen\",\"Task09_Spleen_folds.json\")\n",
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"with open(datalist_file, \"w\", encoding=\"utf-8\") as f:\n",
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" json.dump(datalist_json, f, ensure_ascii=False, indent=4)\n",
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"print(f\"Datalist is saved to {datalist_file}\")"
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]
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},
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{
@@ -144,6 +229,7 @@
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"data_src_cfg = os.path.join(nnunet_root_dir, \"data_src_cfg.yaml\")\n",
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"data_src = {\n",
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" \"modality\": \"CT\",\n",
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" \"dataset_name_or_id\": \"09\",\n",
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" \"datalist\": os.path.join(root_dir, \"Task09_Spleen/msd_task09_spleen_folds.json\"),\n",
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" \"dataroot\": os.path.join(root_dir, \"Task09_Spleen\"),\n",
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"}\n",
@@ -165,12 +251,10 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"scrolled": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"runner.plan_and_process(npfp=2, n_proc=[2, 2, 2])"
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"runner.convert_dataset()"
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]
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},
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{
@@ -179,18 +263,16 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"runner.train(configs=\"3d_fullres\")"
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"runner.plan_and_process(npfp=2, n_proc=[2, 2, 2])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"scrolled": true
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"runner.run(run_train=True, run_find_best_configuration=False, run_predict_ensemble_postprocessing=False)"
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"runner.train_single_model(config=\"3d_fullres\", fold=0)"
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]
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},
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{
@@ -217,9 +299,10 @@
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"source": [
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"%%bash\n",
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"\n",
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"rm nnUNetBundle/configs/inference.json\n",
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"\n",
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"python -m monai.bundle init_bundle nnUNetBundle\n",
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"\n",
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"rm nnUNetBundle/configs/inference.json\n",
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"mkdir -p nnUNetBundle/src\n",
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"touch nnUNetBundle/src/__init__.py\n",
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"which tree && tree nnUNetBundle || true"
@@ -348,8 +431,8 @@
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"outputs": [],
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"source": [
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"nnunet_config = {\n",
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" \"dataset_name_or_id\": \"001\",\n",
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" \"nnunet_trainer\": \"nnUNetTrainer_1epoch\",\n",
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" \"dataset_name_or_id\": \"009\",\n",
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" \"nnunet_trainer\": \"nnUNetTrainer_10epochs\",\n",
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"}\n",
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"\n",
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"bundle_root = \"nnUNetBundle\"\n",
@@ -392,13 +475,17 @@
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"source": [
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"%%bash\n",
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"\n",
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"\n",
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"BUNDLE_ROOT=nnUNetBundle\n",
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"MONAI_DATA_DIRECTORY=/home/maia-user/Documents/GitHub/tutorials/bundle/MONAI/Data\n",
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"\n",
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"python -m monai.bundle run \\\n",
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" --config-file nnUNetBundle/configs/inference.yaml \\\n",
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" --bundle-root nnUNetBundle \\\n",
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" --config-file $BUNDLE_ROOT/configs/inference.yaml \\\n",
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" --bundle-root $BUNDLE_ROOT \\\n",
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" --data_list_file $MONAI_DATA_DIRECTORY/Task09_Spleen/msd_task09_spleen_folds.json \\\n",
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" --output-dir nnUNetBundle/pred_output \\\n",
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" --data_dir /home/maia-user/Tutorials/MONAI/data/Task09_Spleen \\\n",
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" --logging-file nnUNetBundle/configs/logging.conf"
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" --output-dir $BUNDLE_ROOT/pred_output \\\n",
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" --data_dir $MONAI_DATA_DIRECTORY/Task09_Spleen \\\n",
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" --logging-file$BUNDLE_ROOT/configs/logging.conf"
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]
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},
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{
@@ -410,6 +497,8 @@
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"In some cases, you may want to train the nnUNet model from the MONAI Bundle (i.e., without using the nnUNetV2Runner).\n",
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"This is usually the case when the specific training logic is designed to be used with the MONAI Bundle, such as the Active Learning in MONAI Label or Federated Learning in NVFLare using the MONAI Algo implementation.\n",
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"\n",
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"For more details on how to create the nnUNet MONAI Bundle and test all the different components, you can follow the instructions in the [nnUNet MONAI Bundle Notebook](./nnUNet_Bundle.ipynb)\n",
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"\n",
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"This can be done by following the steps below:"
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]
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},
@@ -432,7 +521,6 @@
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" - $import pathlib\n",
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"\n",
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"\n",
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"pymaia_config_dict: \"$json.load(open(@pymaia_config_file))\"\n",
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"bundle_root: .\n",
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"ckpt_dir: \"$@bundle_root + '/models'\"\n",
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"num_classes: 2\n",
@@ -760,7 +848,7 @@
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"kernelspec": {
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"display_name": "MONAI",
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"language": "python",
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"name": "monai"
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {

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