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{
"imports": [
"$from functools import partial",
"$import numpy as np",
"$import torch",
"$from ignite.engine import Events"
],
"device": "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')",
"ckpt_path": "$@bundle_root + '/models/model.pt'",
"dataset_file": "./allimages3label.npz",
"network_def": {
"_target_": "UNet",
"spatial_dims": 2,
"in_channels": 1,
"out_channels": 4,
"channels": [
16,
32,
64,
128,
256
],
"strides": [
2,
2,
2,
2
],
"num_res_units": 2
},
"network": "$@network_def.to(@device)",
"npz": {
"_target_": "NPZDictItemDataset",
"npzfile": "$@dataset_file",
"keys": {
"images": "image",
"segs": "label"
}
},
"partitions": "$monai.data.partition_dataset(np.arange(len(@npz)),(8,2), shuffle=True)",
"train_sub": "$torch.utils.data.Subset(@npz,@partitions[0])",
"eval_sub": "$torch.utils.data.Subset(@npz,@partitions[1])",
"im_shape": "$@train_sub[0]['image'].shape",
"both_keys": [
"image",
"label"
],
"rand_prob": 0.5,
"train_transforms": {
"_target_": "Compose",
"transforms": [
{
"_target_": "CastToTyped",
"keys": "@both_keys",
"dtype": "$(np.float32, np.int32)"
},
{
"_target_": "ScaleIntensityd",
"keys": "image"
},
{
"_target_": "AddChanneld",
"keys": "@both_keys"
},
{
"_target_": "RandAxisFlipd",
"keys": "@both_keys",
"prob": "@rand_prob"
},
{
"_target_": "RandRotate90d",
"keys": "@both_keys",
"prob": "@rand_prob"
},
{
"_target_": "RandSmoothDeformd",
"keys": "@both_keys",
"prob": "@rand_prob",
"spatial_size": "@im_shape",
"rand_size": [
3,
3
],
"pad": 2,
"def_range": 0.1,
"field_mode": "$monai.utils.InterpolateMode.BICUBIC",
"grid_mode": "$(monai.utils.GridSampleMode.BICUBIC, monai.utils.GridSampleMode.NEAREST)",
"align_corners": true
},
{
"_target_": "RandAffined",
"keys": "@both_keys",
"prob": "@rand_prob",
"rotate_range": 0.5,
"translate_range": 50,
"scale_range": 0.25,
"padding_mode": "$monai.utils.GridSamplePadMode.ZEROS",
"mode": "$(monai.utils.GridSampleMode.BILINEAR,monai.utils.GridSampleMode.NEAREST)",
"as_tensor_output": false
},
{
"_target_": "RandSmoothFieldAdjustContrastd",
"keys": "image",
"prob": "@rand_prob",
"spatial_size": "@im_shape",
"rand_size": [
8,
8
],
"gamma": [
0.25,
3
],
"mode": "$monai.utils.InterpolateMode.BICUBIC",
"align_corners": true
},
{
"_target_": "RandSmoothFieldAdjustIntensityd",
"keys": "image",
"prob": "@rand_prob",
"spatial_size": "@im_shape",
"rand_size": [
5,
5
],
"gamma": [
0.1,
1
],
"mode": "$monai.utils.InterpolateMode.BICUBIC",
"align_corners": true
},
{
"_target_": "RandGaussianNoised",
"keys": "image",
"prob": "@rand_prob",
"std": 0.05
},
{
"_target_": "ScaleIntensityd",
"keys": "image"
},
{
"_target_": "CastToTyped",
"keys": "@both_keys",
"dtype": "$(np.float32, np.int32)"
},
{
"_target_": "EnsureTyped",
"keys": "@both_keys"
}
]
},
"eval_transforms": {
"_target_": "Compose",
"transforms": [
{
"_target_": "CastToTyped",
"keys": "@both_keys",
"dtype": "$(np.float32, np.int32)"
},
{
"_target_": "ScaleIntensityd",
"keys": "image"
},
{
"_target_": "AddChanneld",
"keys": "@both_keys"
},
{
"_target_": "EnsureTyped",
"keys": "@both_keys"
}
]
},
"train_dataset": {
"_target_": "CacheDataset",
"data": "@train_sub",
"transform": "@train_transforms"
},
"eval_dataset": {
"_target_": "CacheDataset",
"data": "@eval_sub",
"transform": "@eval_transforms"
},
"train_no_aug_dataset": {
"_target_": "CacheDataset",
"data": "@train_sub",
"transform": "@eval_transforms"
},
"num_iters": 400,
"batch_size": 200,
"num_epochs": 50,
"num_substeps": 5,
"sampler": {
"_target_": "torch.utils.data.WeightedRandomSampler",
"weights": "$torch.ones(len(@train_dataset))",
"replacement": true,
"num_samples": "$@num_iters*@batch_size"
},
"train_dataloader": {
"_target_": "ThreadDataLoader",
"dataset": "@train_dataset",
"batch_size": "@batch_size",
"repeats": "@num_substeps",
"num_workers": 8,
"sampler": "@sampler"
},
"eval_dataloader": {
"_target_": "DataLoader",
"dataset": "@eval_dataset",
"batch_size": "@batch_size",
"num_workers": 4
},
"lossfn": {
"_target_": "DiceLoss",
"include_background": false,
"to_onehot_y": true,
"softmax": true
},
"optimizer": {
"_target_": "torch.optim.Adam",
"params": "$@network.parameters()",
"lr": 0.001
},
"post_transform": {
"_target_": "Compose",
"transforms": [
{
"_target_": "Activationsd",
"keys": "pred",
"softmax": true
},
{
"_target_": "AsDiscreted",
"keys": [
"pred",
"label"
],
"argmax": [
true,
false
],
"to_onehot": 4
}
]
},
"evaluator": {
"_target_": "SupervisedEvaluator",
"device": "@device",
"val_data_loader": "@eval_dataloader",
"network": "@network",
"postprocessing": "@post_transform",
"key_val_metric": {
"val_mean_dice": {
"_target_": "MeanDice",
"include_background": false,
"output_transform": "$monai.handlers.from_engine(['pred', 'label'])"
}
},
"val_handlers": [
{
"_target_": "StatsHandler",
"output_transform": "$lambda x: None"
}
]
},
"handlers": [
{
"_target_": "ValidationHandler",
"validator": "@evaluator",
"epoch_level": true,
"interval": 1
},
{
"_target_": "CheckpointSaver",
"save_dir": "$@bundle_root + '/models'",
"save_dict": {
"model": "@network"
},
"save_interval": 1,
"save_final": true,
"epoch_level": true
},
{
"_target_": "StatsHandler",
"tag_name": "train_loss",
"output_transform": "$monai.handlers.from_engine(['loss'], first=True)"
}
],
"trainer": {
"_target_": "SupervisedTrainer",
"max_epochs": "@num_epochs",
"device": "@device",
"train_data_loader": "@train_dataloader",
"network": "@network",
"loss_function": "@lossfn",
"optimizer": "@optimizer",
"postprocessing": "@post_transform",
"key_train_metric": null,
"train_handlers": "@handlers"
},
"training": [
"$@trainer.run()"
]
}
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