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update the hugging face pipeline for monai tutorial
Signed-off-by: binliu <[email protected]>
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‎hugging_face/hugging_face_pipeline_for_monai.ipynb‎

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@@ -40,11 +40,55 @@
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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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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/usr/local/lib/python3.10/dist-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
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" from .autonotebook import tqdm as notebook_tqdm\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"MONAI version: 1.4.0\n",
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"Numpy version: 1.24.4\n",
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"Pytorch version: 2.5.0a0+872d972e41.nv24.08\n",
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"MONAI flags: HAS_EXT = True, USE_COMPILED = False, USE_META_DICT = False\n",
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"MONAI rev id: 46a5272196a6c2590ca2589029eed8e4d56ff008\n",
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"MONAI __file__: /opt/monai/monai/__init__.py\n",
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"\n",
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"Optional dependencies:\n",
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"Pytorch Ignite version: 0.4.11\n",
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"ITK version: 5.4.0\n",
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"Nibabel version: 5.3.1\n",
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"scikit-image version: 0.24.0\n",
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"scipy version: 1.14.0\n",
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"Pillow version: 10.4.0\n",
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"Tensorboard version: 2.16.2\n",
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"gdown version: 5.2.0\n",
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"TorchVision version: 0.20.0a0\n",
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"tqdm version: 4.66.5\n",
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"lmdb version: 1.5.1\n",
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"psutil version: 6.0.0\n",
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"pandas version: 2.2.2\n",
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"einops version: 0.8.0\n",
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"transformers version: 4.40.2\n",
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"mlflow version: 2.17.0\n",
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"pynrrd version: 1.0.0\n",
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"clearml version: 1.16.5rc2\n",
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"\n",
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"For details about installing the optional dependencies, please visit:\n",
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" https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
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"\n"
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]
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}
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],
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"source": [
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"import copy\n",
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"import logging\n",
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"\n",
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"from monai.transforms import (\n",
@@ -65,7 +109,7 @@
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"from monai.networks.nets import UNet\n",
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"from monai.networks.utils import eval_mode\n",
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"from monai.inferers import SlidingWindowInferer\n",
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"from monai.data import decollate_batch\n",
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"from monai.data import decollate_batch, list_data_collate\n",
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"from monai.config import print_config\n",
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"from monai.apps import download_and_extract\n",
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"import torch\n",
@@ -111,7 +155,7 @@
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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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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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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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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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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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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
@@ -231,7 +275,7 @@
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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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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
@@ -259,16 +303,16 @@
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" \"save_output\",\n",
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" ]\n",
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"\n",
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" def __init__(self, model, config):\n",
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" super().__init__(model=model, config=config)\n",
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" def __init__(self, model, **kwargs):\n",
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" super().__init__(model=model, **kwargs)\n",
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" self.model = model\n",
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" self.preprocessing_transforms = self._init_preprocessing_transforms(**self._preprocess_params)\n",
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" self.inferer = self._init_inferer(**self._forward_params)\n",
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" self.postprocessing_transforms = self._init_postprocessing_transforms(**self._postprocess_params)\n",
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"\n",
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" def _init_preprocessing_transforms(self, image_key=\"image\", load_image=True):\n",
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" def _init_preprocessing_transforms(self, image_key=Keys.IMAGE, load_image=True):\n",
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" transform_list = [LoadImaged(keys=image_key)] if load_image else []\n",
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" transform_list = transform_list.extend(\n",
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" transform_list.extend(\n",
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" [\n",
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" EnsureChannelFirstd(keys=image_key),\n",
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" Orientationd(keys=image_key, axcodes=\"RAS\"),\n",
@@ -282,8 +326,8 @@
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"\n",
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" def _init_postprocessing_transforms(\n",
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" self,\n",
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" pred_key: str = \"pred\",\n",
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" image_key: str = \"image\",\n",
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" pred_key: str = Keys.PRED,\n",
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" image_key: str = Keys.IMAGE,\n",
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" output_dir: str = \"output_directory\",\n",
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" output_ext: str = \".nii.gz\",\n",
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" output_dtype: torch.dtype = torch.float32,\n",
@@ -295,28 +339,24 @@
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" Activationsd(keys=pred_key, softmax=True),\n",
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" Invertd(\n",
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" keys=pred_key,\n",
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" transform=copy.deepcopy(self.preprocessing_transforms),\n",
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" transform=self.preprocessing_transforms,\n",
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" orig_keys=image_key,\n",
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" nearest_interp=False,\n",
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" to_tensor=True,\n",
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" ),\n",
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" AsDiscreted(keys=pred_key, argmax=True),\n",
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" ]\n",
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" transform_list = (\n",
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" transform_list.append(\n",
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" SaveImaged(\n",
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" keys=pred_key,\n",
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" output_dir=output_dir,\n",
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" output_ext=output_ext,\n",
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" output_dtype=output_dtype,\n",
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" output_postfix=output_postfix,\n",
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" separate_folder=separate_folder,\n",
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" )\n",
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" \n",
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" transform_list.append(\n",
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" SaveImaged(\n",
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" keys=pred_key,\n",
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" output_dir=output_dir,\n",
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" output_ext=output_ext,\n",
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" output_dtype=output_dtype,\n",
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" output_postfix=output_postfix,\n",
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" separate_folder=separate_folder,\n",
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" )\n",
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" if save_output\n",
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" else transform_list\n",
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" )\n",
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"\n",
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" )if save_output else transform_list\n",
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" postprocessing_transforms = Compose(transform_list)\n",
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" return postprocessing_transforms\n",
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"\n",
@@ -358,31 +398,39 @@
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" if key not in self.PREPROCESSING_EXTRA_ARGS:\n",
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" logging.warning(f\"Cannot set parameter {key} for preprocessing.\")\n",
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" inputs = self.preprocessing_transforms(inputs)\n",
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" inputs = list_data_collate([inputs])\n",
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" return inputs\n",
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"\n",
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" def _forward(\n",
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" self,\n",
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" inputs,\n",
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" amp: bool = True,\n",
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" ):\n",
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" inputs.to(self.device)\n",
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" self.model.to(self.device)\n",
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" mode = (eval_mode,)\n",
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" outputs = {Keys.IMAGE: inputs, Keys.LABEL: None}\n",
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" inputs[Keys.IMAGE].to(self.device)\n",
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" self.model.unet.to(self.device)\n",
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" mode = eval_mode\n",
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" with mode(self.model):\n",
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" if amp:\n",
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" with torch.autocast(\"cuda\"):\n",
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" outputs[Keys.LABEL] = self.inferer(inputs, self.model)\n",
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" inputs[Keys.PRED] = self.inferer(inputs[Keys.IMAGE], self.model)\n",
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" else:\n",
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" outputs[Keys.LABEL] = self.inferer(inputs, self.model)\n",
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" return outputs\n",
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" inputs[Keys.PRED] = self.inferer(inputs[Keys.IMAGE], self.model)\n",
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" return inputs\n",
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"\n",
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" def postprocess(self, outputs, **kwargs):\n",
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" for key, _ in kwargs.items():\n",
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" for key, value in kwargs.items():\n",
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" if key not in self.POSTPROCESSING_EXTRA_ARGS:\n",
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" logging.warning(f\"Cannot set parameter {key} for postprocessing.\")\n",
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"\n",
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" outputs = self.postprocessing_transforms(decollate_batch(outputs))\n",
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" if (\n",
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" key in self._postprocess_params\n",
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" and value != self._postprocess_params[key]\n",
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" ) or (key not in self._postprocess_params):\n",
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" self._postprocess_params.update(kwargs)\n",
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" self.postprocessing_transforms = self._init_postprocessing_transforms(\n",
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" **self._postprocess_params\n",
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" )\n",
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" outputs = decollate_batch(outputs)\n",
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" outputs = self.postprocessing_transforms(outputs)\n",
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" return outputs"
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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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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"2025-02-28 16:34:41,094 INFO image_writer.py:197 - writing: output_directory/spleen_1/spleen_1_seg.nii.gz\n",
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"2025-02-28 16:34:47,260 INFO image_writer.py:197 - writing: output_directory/spleen_11/spleen_11_seg.nii.gz\n"
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]
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}
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],
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"source": [
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"config = MONAIUNetConfig()\n",
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"monai_unet = MONAIUNet(config)\n",
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"pipeline = SpleenCTSegmentationPipeline(model=monai_unet)\n",
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"output = pipeline(data_dicts[0])"
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"pipeline = SpleenCTSegmentationPipeline(model=monai_unet, device=torch.device(\"cuda:0\"))\n",
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"output = pipeline(data_dicts[:2])"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "nim-test",
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"display_name": "Python 3",
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"language": "python",
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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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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"version": "3.10.15"
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.12"
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}
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},
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"nbformat": 4,

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