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GennUNet

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Release model

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  1. inference.ipynb +167 -0
  2. input_images/.DS_Store +0 -0
  3. input_images/img0027_0000.nii.gz +3 -0
  4. nnUNet_weights/dataset.json +23 -0
  5. nnUNet_weights/fold_0/checkpoint_best.pth +3 -0
  6. nnUNet_weights/fold_1/checkpoint_best.pth +3 -0
  7. nnUNet_weights/fold_2/checkpoint_best.pth +3 -0
  8. nnUNet_weights/fold_3/checkpoint_best.pth +3 -0
  9. nnUNet_weights/fold_4/checkpoint_best.pth +3 -0
  10. nnUNet_weights/plans.json +294 -0
  11. output_images/dataset.json +22 -0
  12. output_images/img0027_gt.nii.gz +3 -0
  13. output_images/img0027_pred.nii.gz +3 -0
  14. output_images/plans.json +294 -0
  15. output_images/predict_from_raw_data_args.json +17 -0
inference.ipynb ADDED
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+ {
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+ "cells": [
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+ {
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+ "cell_type": "code",
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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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+ "import torch\n",
10
+ "from batchgenerators.utilities.file_and_folder_operations import join\n",
11
+ "from nnunetv2.inference.predict_from_raw_data import nnUNetPredictor\n",
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+ "from nnunetv2.imageio.simpleitk_reader_writer import SimpleITKIO"
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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": 2,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "# instantiate the nnUNetPredictor\n",
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+ "predictor = nnUNetPredictor(\n",
23
+ " tile_step_size=0.5, # 50% overlap between adjacent tiles\n",
24
+ " use_gaussian=True, # Apply Gaussian weighting to smooth tile edges\n",
25
+ " use_mirroring=True, # Enable test-time augmentation via flipping\n",
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+ " perform_everything_on_device=True, # Perform all steps (preprocessing, prediction) on GPU\n",
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+ " device=torch.device('cuda', 0), # Use the first GPU (cuda:0) for computations\n",
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+ " verbose=False, # Disable detailed output logs during prediction\n",
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+ " verbose_preprocessing=False, # Disable logs during preprocessing\n",
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+ " allow_tqdm=True # Show progress bar during long tasks\n",
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+ ")"
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+ ]
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+ },
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+ {
35
+ "cell_type": "code",
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+ "execution_count": null,
37
+ "metadata": {},
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+ "outputs": [],
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+ "source": [
40
+ "# initializes the network architecture, loads the checkpoint\n",
41
+ "predictor.initialize_from_trained_model_folder(\n",
42
+ " \"./nnUNet_weights\", # Path to the model weights\n",
43
+ " use_folds=(0,1,2,3,4), # Use all 5 folds (for cross-validation)\n",
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+ " checkpoint_name='checkpoint_best.pth', # File name of model checkpoints (all must be equal)\n",
45
+ ")"
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+ ]
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+ },
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+ {
49
+ "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": [
54
+ "# variant 1: give input and output folders\n",
55
+ "# Note: if specific file path is provided, no need for \"_0000.nii.gz\" file ending;\n",
56
+ "# Note: if input folder path is provided, the input files MUST include \"_0000.nii.gz\" ending.\n",
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+ "predictor.predict_from_files(\n",
58
+ " \"./input_images\", # Input folder with image files\n",
59
+ " \"./output_images\", # Output folder for predictions\n",
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+ " save_probabilities=False, # Do not save the predicted probabilities, just the segmentation\n",
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+ " overwrite=False, # Do not overwrite existing results in the output folder\n",
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+ " num_processes_preprocessing=2, # Number of processes for preprocessing\n",
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+ " num_processes_segmentation_export=2, # Number of processes for exporting the segmentation\n",
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+ " folder_with_segs_from_prev_stage=None, # No previous stage segmentations used\n",
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+ " num_parts=1, # Number of parts to divide the prediction task into\n",
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+ " part_id=0 # ID of the current part (only one part in this case)\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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+ "# variant 2.1, use list of files as inputs. Note how we use nested lists!!!\n",
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+ "indir = \"./input_images\" # Input folder with image files\n",
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+ "outdir = \"./output_images\" # Output folder for predictions\n",
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+ "predictor.predict_from_files(\n",
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+ " [[join(indir, 'img0027_0000.nii.gz')]],\n",
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+ " [join(outdir, 'img0027_pred.nii.gz')],\n",
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+ " save_probabilities=False,\n",
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+ " overwrite=False,\n",
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+ " num_processes_preprocessing=2,\n",
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+ " num_processes_segmentation_export=2,\n",
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+ " folder_with_segs_from_prev_stage=None,\n",
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+ " num_parts=1,\n",
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+ " part_id=0\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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+ "# variant 2.2, returns segmentations (The predicted segmentations will be returned if the output_files are not specified)\n",
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+ "indir = \"./input_images\"\n",
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+ "predicted_segmentations = predictor.predict_from_files(\n",
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+ " [[join(indir, 'img0027_0000.nii.gz')], [join(indir, 'img0027_0000.nii.gz')]], # Example of several input images with repeated sample\n",
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+ " None,\n",
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+ " save_probabilities=False,\n",
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+ " overwrite=True,\n",
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+ " num_processes_preprocessing=2,\n",
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+ " num_processes_segmentation_export=2,\n",
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+ " folder_with_segs_from_prev_stage=None,\n",
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+ " num_parts=1,\n",
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+ " part_id=0)"
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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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+ "# variant 3.1, predict a list of numpy arrays\n",
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+ "indir = \"./input_images\"\n",
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+ "img, props = SimpleITKIO().read_images([join(indir, 'img0027_0000.nii.gz')])\n",
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+ "\n",
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+ "# we do not set output files so that the segmentations will be returned. You can of course also specify output\n",
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+ "# files instead (no return value on that case)\n",
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+ "ret = predictor.predict_from_list_of_npy_arrays(\n",
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+ " [img,],\n",
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+ " None,\n",
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+ " [props,],\n",
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+ " None,\n",
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+ " 2,\n",
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+ " save_probabilities=False,\n",
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+ " num_processes_segmentation_export=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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+ "outputs": [],
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+ "source": [
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+ "# variant 3.2, predict a single numpy array\n",
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+ "img, props = SimpleITKIO().read_images([\"./input_images/img0027_0000.nii.gz\"])\n",
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+ "ret = predictor.predict_single_npy_array(img, props, None, None, False)"
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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": "Python 3 (ipykernel)",
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+ "language": "python",
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+ "name": "python3"
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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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+ "file_extension": ".py",
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+ "mimetype": "text/x-python",
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+ "name": "python",
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+ "nbconvert_exporter": "python",
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+ "pygments_lexer": "ipython3",
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+ "version": "3.10.13"
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+ }
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+ },
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+ "nbformat": 4,
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+ "nbformat_minor": 4
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+ }
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