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[DLMED] draft config
Signed-off-by: Nic Ma <[email protected]>
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‎modules/bundles/spleen_segmentation/configs/train.json

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"determinism": "$monai.utils.set_determinism(seed=123)",
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"cudnn_opt": "$setattr(torch.backends.cudnn, 'benchmark', True)",
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"device": "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')",
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"ckpt_dir": "/workspace/data/tutorials/modules/bundles/spleen_segmentation/models",
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"dataset_dir": "/workspace/data/Task09_Spleen",
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"ckpt_dir": "/workspace/data/medical/tutorials/modules/bundles/spleen_segmentation/models",
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"dataset_dir": "/workspace/data/medical/Task09_Spleen",
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"images": "$list(sorted(glob.glob(@dataset_dir + '/imagesTr/*.nii.gz')))",
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"labels": "$list(sorted(glob.glob(@dataset_dir + '/labelsTr/*.nii.gz')))",
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"network_def": {
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"_target_": "DataLoader",
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"dataset": "@train#dataset",
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"batch_size": 2,
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"shuffle": false,
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"shuffle": true,
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"num_workers": 4
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},
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"inferer": {
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},
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"trainer": {
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"_target_": "SupervisedTrainer",
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"_requires_": ["@determinism", "@cudnn_opt"],
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"_requires_": ["@ddp_init", "@determinism", "@cudnn_opt"],
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"max_epochs": 100,
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"device": "@device",
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"train_data_loader": "@train#dataloader",

‎modules/bundles/spleen_segmentation/configs/train_multi_gpu.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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"$import ignite",
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"$import torch.distributed as dist"
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],
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"ddp_init": "$dist.init_process_group(backend='nccl', init_method='env://')",
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"determinism": "$monai.utils.set_determinism(seed=123)",
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"cudnn_opt": "$setattr(torch.backends.cudnn, 'benchmark', True)",
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"device": "$torch.device(f'cuda:{dist.get_rank()}')",
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"set_device": "$torch.cuda.set_device(@device)",
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"ckpt_dir": "/workspace/data/medical/tutorials/modules/bundles/spleen_segmentation/models",
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"dataset_dir": "/workspace/data/medical/Task09_Spleen",
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"images": "$list(sorted(glob.glob(@dataset_dir + '/imagesTr/*.nii.gz')))",
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"labels": "$list(sorted(glob.glob(@dataset_dir + '/labelsTr/*.nii.gz')))",
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"network_def": {
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"_target_": "UNet",
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"spatial_dims": 3,
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"in_channels": 1,
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"out_channels": 2,
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"channels": [16, 32, 64, 128, 256],
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"strides": [2, 2, 2, 2],
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"num_res_units": 2,
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"norm": "batch"
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},
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"gpu_net": "$@network_def.to(@device)",
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"loss": {
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"_target_": "DiceCELoss",
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"to_onehot_y": true,
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"softmax": true,
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"squared_pred": true,
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"batch": true
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},
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"optimizer": {
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"_target_": "torch.optim.Adam",
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"params": "$@gpu_net.parameters()",
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"lr": 1e-4
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},
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"network": {
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"_target_": "torch.nn.parallel.DistributedDataParallel",
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"module": "@gpu_net",
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"device_ids": ["@device"]
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},
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"train": {
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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", "label"]
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},
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{
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"_target_": "EnsureChannelFirstd",
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"keys": ["image", "label"]
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},
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{
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"_target_": "Orientationd",
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"keys": ["image", "label"],
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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", "label"],
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"pixdim": [1.5, 1.5, 2.0],
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"mode": ["bilinear", "nearest"]
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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": -57,
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"a_max": 164,
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"b_min": 0,
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"b_max": 1,
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"clip": true
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},
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{
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"_target_": "RandCropByPosNegLabeld",
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"keys": ["image", "label"],
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"label_key": "label",
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"spatial_size": [96, 96, 96],
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"pos": 1,
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"neg": 1,
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"num_samples": 4,
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"image_key": "image",
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"image_threshold": 0
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},
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{
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"_target_": "EnsureTyped",
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"keys": ["image", "label"]
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}
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]
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},
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"dataset": {
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"_target_": "CacheDataset",
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"data": "$[{'image': i, 'label': l} for i, l in zip(@images[:-9], @labels[:-9])]",
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"transform": "@train#preprocessing",
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"cache_rate": 1.0,
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"num_workers": 4
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},
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"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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"dataloader": {
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"_target_": "DataLoader",
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"dataset": "@train#dataset",
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"sampler": "@train#sampler",
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"batch_size": 2,
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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_": "SimpleInferer"
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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_": "AsDiscreted",
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"keys": ["pred", "label"],
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"argmax": [true, false],
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"to_onehot": 2
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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_": "ValidationHandler",
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"validator": "@validate#evaluator",
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"epoch_level": true,
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"interval": 5
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},
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{
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"_target_": "StatsHandler",
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"_disabled_": "$dist.get_rank() > 0",
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"tag_name": "train_loss",
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"output_transform": "$monai.handlers.from_engine(['loss'], first=True)"
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},
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{
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"_target_": "TensorBoardStatsHandler",
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"_disabled_": "$dist.get_rank() > 0",
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"log_dir": "eval",
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"tag_name": "train_loss",
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"output_transform": "$monai.handlers.from_engine(['loss'], first=True)"
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}
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],
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"key_metric": {
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"train_accuracy": {
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"_target_": "ignite.metrics.Accuracy",
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"output_transform": "$monai.handlers.from_engine(['pred', 'label'])"
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}
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},
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"trainer": {
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"_target_": "SupervisedTrainer",
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"_requires_": ["@ddp_init", "@set_device", "@determinism", "@cudnn_opt"],
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"max_epochs": 100,
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"device": "@device",
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"train_data_loader": "@train#dataloader",
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"network": "@network",
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"loss_function": "@loss",
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"optimizer": "@optimizer",
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"inferer": "@train#inferer",
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"postprocessing": "@train#postprocessing",
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"key_train_metric": "@train#key_metric",
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"train_handlers": "@train#handlers",
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"amp": true
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}
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},
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"validate": {
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"preprocessing": {
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"_target_": "Compose",
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"transforms": [
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"%train#preprocessing#transforms#0",
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"%train#preprocessing#transforms#1",
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"%train#preprocessing#transforms#2",
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"%train#preprocessing#transforms#3",
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"%train#preprocessing#transforms#4",
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"%train#preprocessing#transforms#6"
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]
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},
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"dataset": {
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"_target_": "CacheDataset",
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"data": "$[{'image': i, 'label': l} for i, l in zip(@images[-9:], @labels[-9:])]",
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"transform": "@validate#preprocessing",
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"cache_rate": 1.0
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},
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"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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"dataloader": {
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"_target_": "DataLoader",
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"dataset": "@validate#dataset",
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"sampler": "@validate#sampler",
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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": [96, 96, 96],
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"sw_batch_size": 4,
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"overlap": 0.5
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},
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"postprocessing": "%train#postprocessing",
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"handlers": [
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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_": "TensorBoardStatsHandler",
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"log_dir": "eval",
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"iteration_log": false
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},
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{
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"_target_": "CheckpointSaver",
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"save_dir": "@ckpt_dir",
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"save_dict": {"model": "@network"},
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"save_key_metric": true,
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"key_metric_filename": "model.pt"
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}
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],
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"key_metric": {
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"val_mean_dice": {
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"_target_": "MeanDice",
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"include_background": false,
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"output_transform": "$monai.handlers.from_engine(['pred', 'label'])"
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}
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},
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"additional_metrics": {
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"val_accuracy": {
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"_target_": "ignite.metrics.Accuracy",
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"output_transform": "$monai.handlers.from_engine(['pred', 'label'])"
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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": "@validate#dataloader",
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"network": "@network",
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"inferer": "@validate#inferer",
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"postprocessing": "@validate#postprocessing",
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"key_val_metric": "@validate#key_metric",
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"additional_metrics": "@validate#additional_metrics",
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"val_handlers": "$@validate#handlers if dist.get_rank() > 0 else None",
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"amp": true
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}
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}
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}

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