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Merge pull request #27 from Project-MONAI/swin_unetr_btcv
add swin_unetr bundle
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‎models/swin_unetr_btcv_segmentation/configs/evaluate.json‎

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{
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"validate#postprocessing": {
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"_target_": "Compose",
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"transforms": [
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{
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"_target_": "Activationsd",
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"keys": "pred",
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"softmax": true
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},
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{
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"_target_": "Invertd",
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"keys": [
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"pred",
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"label"
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],
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"transform": "@validate#preprocessing",
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"orig_keys": "image",
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"meta_key_postfix": "meta_dict",
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"nearest_interp": [
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false,
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true
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],
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"to_tensor": true
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},
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{
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"_target_": "AsDiscreted",
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"keys": [
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"pred",
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"label"
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],
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"argmax": [
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true,
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false
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],
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"to_onehot": 14
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},
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{
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"_target_": "SaveImaged",
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"keys": "pred",
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"meta_keys": "pred_meta_dict",
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"output_dir": "@output_dir",
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"resample": false,
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"squeeze_end_dims": true
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}
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]
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},
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"validate#handlers": [
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{
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"_target_": "CheckpointLoader",
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"load_path": "$@ckpt_dir + '/model.pt'",
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"load_dict": {
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"model": "@network"
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}
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},
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{
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"_target_": "StatsHandler",
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"iteration_log": false
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},
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{
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"_target_": "MetricsSaver",
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"save_dir": "@output_dir",
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"metrics": [
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"val_mean_dice",
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"val_acc"
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],
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"metric_details": [
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"val_mean_dice"
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],
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"batch_transform": "$monai.handlers.from_engine(['image_meta_dict'])",
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"summary_ops": "*"
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}
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],
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"evaluating": [
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"$setattr(torch.backends.cudnn, 'benchmark', True)",
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"$@validate#evaluator.run()"
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]
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}

‎models/swin_unetr_btcv_segmentation/configs/inference.json‎

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{
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"imports": [
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"$import glob",
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"$import os"
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],
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"bundle_root": "/workspace/MONAI_Bundle/swin_unetr_btcv_segmentation/",
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"output_dir": "$@bundle_root + '/eval'",
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"dataset_dir": "/dataset/dataset0",
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"datalist": "$list(sorted(glob.glob(@dataset_dir + '/imagesTs/*.nii.gz')))",
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"device": "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')",
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"network_def": {
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"_target_": "SwinUNETR",
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"spatial_dims": 3,
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"img_size": 96,
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"in_channels": 1,
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"out_channels": 14,
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"feature_size": 48,
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"use_checkpoint": true
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},
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"network": "$@network_def.to(@device)",
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"preprocessing": {
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"_target_": "Compose",
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"transforms": [
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{
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"_target_": "LoadImaged",
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"keys": "image"
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},
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{
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"_target_": "EnsureChannelFirstd",
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"keys": "image"
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},
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{
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"_target_": "Orientationd",
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"keys": "image",
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"axcodes": "RAS"
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},
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{
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"_target_": "Spacingd",
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"keys": "image",
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"pixdim": [
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1.5,
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1.5,
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2.0
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],
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"mode": "bilinear"
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},
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{
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"_target_": "ScaleIntensityRanged",
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"keys": "image",
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"a_min": -175,
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"a_max": 250,
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"b_min": 0.0,
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"b_max": 1.0,
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"clip": true
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},
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{
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"_target_": "EnsureTyped",
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"keys": "image"
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}
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]
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},
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"dataset": {
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"_target_": "Dataset",
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"data": "$[{'image': i} for i in @datalist]",
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"transform": "@preprocessing"
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},
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"dataloader": {
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"_target_": "DataLoader",
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"dataset": "@dataset",
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"batch_size": 1,
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"shuffle": false,
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"num_workers": 4
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},
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"inferer": {
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"_target_": "SlidingWindowInferer",
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"roi_size": [
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96,
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96,
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96
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],
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"sw_batch_size": 4,
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"overlap": 0.5
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},
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"postprocessing": {
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"_target_": "Compose",
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"transforms": [
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{
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"_target_": "Activationsd",
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"keys": "pred",
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"softmax": true
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},
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{
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"_target_": "Invertd",
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"keys": "pred",
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"transform": "@preprocessing",
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"orig_keys": "image",
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"meta_key_postfix": "meta_dict",
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"nearest_interp": false,
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"to_tensor": true
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},
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{
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"_target_": "AsDiscreted",
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"keys": "pred",
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"argmax": true
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},
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{
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"_target_": "SaveImaged",
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"keys": "pred",
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"meta_keys": "pred_meta_dict",
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"output_dir": "@output_dir"
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}
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]
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},
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"handlers": [
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{
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"_target_": "CheckpointLoader",
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"load_path": "$@bundle_root + '/models/model.pt'",
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"load_dict": {
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"model": "@network"
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}
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},
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{
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"_target_": "StatsHandler",
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"iteration_log": false
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}
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],
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"evaluator": {
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"_target_": "SupervisedEvaluator",
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"device": "@device",
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"val_data_loader": "@dataloader",
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"network": "@network",
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"inferer": "@inferer",
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"postprocessing": "@postprocessing",
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"val_handlers": "@handlers",
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"amp": true
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},
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"evaluating": [
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"$setattr(torch.backends.cudnn, 'benchmark', True)",
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"[email protected]()"
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]
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}

‎models/swin_unetr_btcv_segmentation/configs/logging.conf‎

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[loggers]
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keys=root
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[handlers]
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keys=consoleHandler
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[formatters]
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keys=fullFormatter
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[logger_root]
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level=INFO
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handlers=consoleHandler
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[handler_consoleHandler]
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class=StreamHandler
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level=INFO
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formatter=fullFormatter
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args=(sys.stdout,)
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[formatter_fullFormatter]
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format=%(asctime)s - %(name)s - %(levelname)s - %(message)s

‎models/swin_unetr_btcv_segmentation/configs/metadata.json‎

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{
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"schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
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"version": "0.1.0",
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"changelog": {
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"0.1.0": "complete the model package",
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"0.0.1": "initialize the model package structure"
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},
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"monai_version": "0.9.0rc1+19.g61a0dc35",
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"pytorch_version": "1.10.0",
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"numpy_version": "1.21.2",
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"optional_packages_version": {
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"nibabel": "3.2.1"
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},
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"task": "BTCV multi-organ segmentation",
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"description": "A pre-trained model for volumetric (3D) multi-organ segmentation from CT image",
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"authors": "MONAI team",
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"copyright": "Copyright (c) MONAI Consortium",
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"data_source": "RawData.zip from https://www.synapse.org/#!Synapse:syn3193805/wiki/217752/",
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"data_type": "nibabel",
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"image_classes": "single channel data, intensity scaled to [0, 1]",
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"label_classes": "multi-channel data,0:background,1:spleen, 2:Right Kidney, 3:Left Kideny, 4:Gallbladder, 5:Esophagus, 6:Liver, 7:Stomach, 8:Aorta, 9:IVC, 10:Portal and Splenic Veins, 11:Pancreas, 12:Right adrenal gland, 13:Left adrenal gland",
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"pred_classes": "14 channels OneHot data, 0:background,1:spleen, 2:Right Kidney, 3:Left Kideny, 4:Gallbladder, 5:Esophagus, 6:Liver, 7:Stomach, 8:Aorta, 9:IVC, 10:Portal and Splenic Veins, 11:Pancreas, 12:Right adrenal gland, 13:Left adrenal gland",
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"eval_metrics": {
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"mean_dice": 0.8283
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},
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"intended_use": "This is an example, not to be used for diagnostic purposes",
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"references": [
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"Hatamizadeh, Ali, et al. 'Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images. arXiv preprint arXiv:2201.01266 (2022). https://arxiv.org/abs/2201.01266.",
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"Tang, Yucheng, et al. 'Self-supervised pre-training of swin transformers for 3d medical image analysis. arXiv preprint arXiv:2111.14791 (2021). https://arxiv.org/abs/2111.14791."
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],
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"network_data_format": {
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"inputs": {
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"image": {
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"type": "image",
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"format": "hounsfield",
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"modality": "CT",
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"num_channels": 1,
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"spatial_shape": [
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96,
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96,
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96
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],
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"dtype": "float32",
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"value_range": [
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0,
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1
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],
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"is_patch_data": true,
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"channel_def": {
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"0": "image"
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}
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}
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},
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"outputs": {
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"pred": {
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"type": "image",
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"format": "segmentation",
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"num_channels": 14,
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"spatial_shape": [
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96,
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96,
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96
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],
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"dtype": "float32",
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"value_range": [
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0,
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1
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],
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"is_patch_data": true,
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"channel_def": {
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"0": "background",
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"1": "spleen",
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"2": "Right Kidney",
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"3": "Left Kideny",
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"4": "Gallbladder",
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"5": "Esophagus",
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"6": "Liver",
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"7": "Stomach",
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"8": "Aorta",
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"9": "IVC",
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"10": "Portal and Splenic Veins",
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"11": "Pancreas",
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"12": "Right adrenal gland",
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"13": "Left adrenal gland"
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}
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}
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}
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}
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}

‎models/swin_unetr_btcv_segmentation/configs/multi_gpu_train.json‎

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{
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"device": "$torch.device(f'cuda:{dist.get_rank()}')",
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"network": {
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"_target_": "torch.nn.parallel.DistributedDataParallel",
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"module": "$@network_def.to(@device)",
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"device_ids": [
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"@device"
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]
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},
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"train#sampler": {
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"_target_": "DistributedSampler",
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"dataset": "@train#dataset",
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"even_divisible": true,
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"shuffle": true
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},
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"train#dataloader#sampler": "@train#sampler",
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"train#dataloader#shuffle": false,
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"train#trainer#train_handlers": "$@train#handlers[: -2 if dist.get_rank() > 0 else None]",
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"validate#sampler": {
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"_target_": "DistributedSampler",
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"dataset": "@validate#dataset",
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"even_divisible": false,
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"shuffle": false
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},
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"validate#dataloader#sampler": "@validate#sampler",
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"validate#evaluator#val_handlers": "$None if dist.get_rank() > 0 else @validate#handlers",
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"training": [
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"$import torch.distributed as dist",
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"$dist.init_process_group(backend='nccl')",
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"$torch.cuda.set_device(@device)",
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"$monai.utils.set_determinism(seed=123)",
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"$setattr(torch.backends.cudnn, 'benchmark', True)",
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"$@train#trainer.run()",
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"$dist.destroy_process_group()"
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]
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}

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