monailabel.tasks.train.basic_train module

class monailabel.tasks.train.basic_train.BasicTrainTask(model_dir, description=None, config=None, amp=True, load_path=None, load_dict=None, publish_path=None, stats_path=None, train_save_interval=50, val_interval=1, final_filename='checkpoint_final.pt', key_metric_filename='model.pt')

Bases: monailabel.interfaces.tasks.train.TrainTask

This provides Basic Train Task to train a model using SupervisedTrainer and SupervisedEvaluator from MONAI

Parameters

• model_dir – Base Model Dir to save the model checkpoints, events etc…

• description – Description for this task

• config – K,V pairs to be part of user config

• amp – Enable AMP for training

• load_path – Initialize model from existing checkpoint (pre-trained)

• load_dict – Provide dictionary to load from checkpoint. If None, then net will be loaded

• publish_path – Publish path for best trained model (based on best key metric)

• stats_path – Path to save the train stats

• train_save_interval – checkpoint save interval for training

• val_interval – validation interval (run every x epochs)

• final_filename – name of final checkpoint that will be saved

• key_metric_filename – best key metric model file name

__init__(model_dir, description=None, config=None, amp=True, load_path=None, load_dict=None, publish_path=None, stats_path=None, train_save_interval=50, val_interval=1, final_filename='checkpoint_final.pt', key_metric_filename='model.pt')

Parameters

• model_dir – Base Model Dir to save the model checkpoints, events etc…

• description – Description for this task

• config – K,V pairs to be part of user config

• amp – Enable AMP for training

• load_path – Initialize model from existing checkpoint (pre-trained)

• load_dict – Provide dictionary to load from checkpoint. If None, then net will be loaded

• publish_path – Publish path for best trained model (based on best key metric)

• stats_path – Path to save the train stats

• train_save_interval – checkpoint save interval for training

• val_interval – validation interval (run every x epochs)

• final_filename – name of final checkpoint that will be saved

• key_metric_filename – best key metric model file name

config()

event_names()

load_path(output_dir, pretrained=True)

abstract loss_function()

abstract network()

abstract optimizer()

partition_datalist(request, datalist, shuffle=True)

stats()

train_additional_metrics()

train_data_loader(datalist, batch_size=1, num_workers=0, cached=False)

train_handlers(output_dir, events_dir, evaluator)

train_inferer()

train_iteration_update()

train_key_metric()

abstract train_post_transforms()

abstract train_pre_transforms()

val_additional_metrics()

val_data_loader(datalist, batch_size=1, num_workers=0, cached=False)

val_handlers(output_dir, events_dir)

abstract val_inferer()

val_iteration_update()

val_key_metric()

val_post_transforms()

val_pre_transforms()

monailabel.tasks.train package monailabel.tasks.train.handler module