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{
"imports": [
"$import glob",
"$import os"
],
"bundle_root": "/workspace/brats_mri_segmentation",
"ckpt_dir": "$@bundle_root + '/models'",
"output_dir": "$@bundle_root + '/eval'",
"data_list_file_path": "$@bundle_root + '/configs/datalist.json'",
"data_file_base_dir": "/workspace/data/medical/brats2018challenge",
"train_datalist": "$monai.data.load_decathlon_datalist(@data_list_file_path, data_list_key='training', base_dir=@data_file_base_dir)",
"val_datalist": "$monai.data.load_decathlon_datalist(@data_list_file_path, data_list_key='validation', base_dir=@data_file_base_dir)",
"device": "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')",
"epochs": 300,
"num_interval_per_valid": 1,
"learning_rate": 0.0001,
"amp": true,
"network_def": {
"_target_": "SegResNet",
"blocks_down": [
1,
2,
2,
4
],
"blocks_up": [
1,
1,
1
],
"init_filters": 16,
"in_channels": 4,
"out_channels": 3,
"dropout_prob": 0.2
},
"network": "$@network_def.to(@device)",
"loss": {
"_target_": "DiceLoss",
"smooth_nr": 0,
"smooth_dr": 1e-05,
"squared_pred": true,
"to_onehot_y": false,
"sigmoid": true
},
"optimizer": {
"_target_": "torch.optim.Adam",
"params": "$@network.parameters()",
"lr": "@learning_rate",
"weight_decay": 1e-05
},
"lr_scheduler": {
"_target_": "torch.optim.lr_scheduler.CosineAnnealingLR",
"optimizer": "@optimizer",
"T_max": "@epochs"
},
"train": {
"preprocessing_transforms": [
{
"_target_": "LoadImaged",
"keys": [
"image",
"label"
]
},
{
"_target_": "ConvertToMultiChannelBasedOnBratsClassesd",
"keys": "label"
},
{
"_target_": "NormalizeIntensityd",
"keys": "image",
"nonzero": true,
"channel_wise": true
}
],
"random_transforms": [
{
"_target_": "RandSpatialCropd",
"keys": [
"image",
"label"
],
"roi_size": [
224,
224,
144
],
"random_size": false
},
{
"_target_": "RandFlipd",
"keys": [
"image",
"label"
],
"prob": 0.5,
"spatial_axis": 0
},
{
"_target_": "RandFlipd",
"keys": [
"image",
"label"
],
"prob": 0.5,
"spatial_axis": 1
},
{
"_target_": "RandFlipd",
"keys": [
"image",
"label"
],
"prob": 0.5,
"spatial_axis": 2
},
{
"_target_": "RandScaleIntensityd",
"keys": "image",
"factors": 0.1,
"prob": 1.0
},
{
"_target_": "RandShiftIntensityd",
"keys": "image",
"offsets": 0.1,
"prob": 1.0
}
],
"final_transforms": [
{
"_target_": "ToTensord",
"keys": [
"image",
"label"
]
}
],
"preprocessing": {
"_target_": "Compose",
"transforms": "$@train#preprocessing_transforms + @train#random_transforms + @train#final_transforms"
},
"dataset": {
"_target_": "Dataset",
"data": "@train_datalist",
"transform": "@train#preprocessing"
},
"dataloader": {
"_target_": "DataLoader",
"dataset": "@train#dataset",
"batch_size": 1,
"shuffle": true,
"num_workers": 4
},
"inferer": {
"_target_": "SimpleInferer"
},
"postprocessing": {
"_target_": "Compose",
"transforms": [
{
"_target_": "Activationsd",
"keys": "pred",
"sigmoid": true
},
{
"_target_": "AsDiscreted",
"keys": "pred",
"threshold": 0.5
}
]
},
"handlers": [
{
"_target_": "LrScheduleHandler",
"lr_scheduler": "@lr_scheduler",
"print_lr": true
},
{
"_target_": "ValidationHandler",
"validator": "@validate#evaluator",
"epoch_level": true,
"interval": "@num_interval_per_valid"
},
{
"_target_": "StatsHandler",
"tag_name": "train_loss",
"output_transform": "$monai.handlers.from_engine(['loss'], first=True)"
},
{
"_target_": "TensorBoardStatsHandler",
"log_dir": "@output_dir",
"tag_name": "train_loss",
"output_transform": "$monai.handlers.from_engine(['loss'], first=True)"
}
],
"key_metric": {
"train_mean_dice": {
"_target_": "MeanDice",
"include_background": true,
"output_transform": "$monai.handlers.from_engine(['pred', 'label'])"
}
},
"trainer": {
"_target_": "SupervisedTrainer",
"max_epochs": "@epochs",
"device": "@device",
"train_data_loader": "@train#dataloader",
"network": "@network",
"loss_function": "@loss",
"optimizer": "@optimizer",
"inferer": "@train#inferer",
"postprocessing": "@train#postprocessing",
"key_train_metric": "@train#key_metric",
"train_handlers": "@train#handlers",
"amp": "@amp"
}
},
"validate": {
"preprocessing": {
"_target_": "Compose",
"transforms": "$@train#preprocessing_transforms + @train#final_transforms"
},
"dataset": {
"_target_": "Dataset",
"data": "@val_datalist",
"transform": "@validate#preprocessing"
},
"dataloader": {
"_target_": "DataLoader",
"dataset": "@validate#dataset",
"batch_size": 1,
"shuffle": false,
"num_workers": 4
},
"inferer": {
"_target_": "SlidingWindowInferer",
"roi_size": [
240,
240,
160
],
"sw_batch_size": 1,
"overlap": 0.5
},
"postprocessing": {
"_target_": "Compose",
"transforms": [
{
"_target_": "Activationsd",
"keys": "pred",
"sigmoid": true
},
{
"_target_": "AsDiscreted",
"keys": "pred",
"threshold": 0.5
},
{
"_target_": "SplitChanneld",
"keys": [
"pred",
"label"
],
"output_postfixes": [
"tc",
"wt",
"et"
]
}
]
},
"handlers": [
{
"_target_": "StatsHandler",
"iteration_log": false
},
{
"_target_": "TensorBoardStatsHandler",
"log_dir": "@output_dir",
"iteration_log": false
},
{
"_target_": "CheckpointSaver",
"save_dir": "@ckpt_dir",
"save_dict": {
"model": "@network"
},
"save_key_metric": true,
"key_metric_filename": "model.pt"
}
],
"key_metric": {
"val_mean_dice": {
"_target_": "MeanDice",
"include_background": true,
"output_transform": "$monai.handlers.from_engine(['pred', 'label'])"
}
},
"additional_metrics": {
"val_mean_dice_tc": {
"_target_": "MeanDice",
"include_background": true,
"output_transform": "$monai.handlers.from_engine(['pred_tc', 'label_tc'])"
},
"val_mean_dice_wt": {
"_target_": "MeanDice",
"include_background": true,
"output_transform": "$monai.handlers.from_engine(['pred_wt', 'label_wt'])"
},
"val_mean_dice_et": {
"_target_": "MeanDice",
"include_background": true,
"output_transform": "$monai.handlers.from_engine(['pred_et', 'label_et'])"
}
},
"evaluator": {
"_target_": "SupervisedEvaluator",
"device": "@device",
"val_data_loader": "@validate#dataloader",
"network": "@network",
"inferer": "@validate#inferer",
"postprocessing": "@validate#postprocessing",
"key_val_metric": "@validate#key_metric",
"additional_metrics": "@validate#additional_metrics",
"val_handlers": "@validate#handlers",
"amp": "@amp"
}
},
"training": [
"$monai.utils.set_determinism(seed=123)",
"$setattr(torch.backends.cudnn, 'benchmark', True)",
"$@train#trainer.run()"
]
}
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