dlk.core.callbacks package

Submodules

dlk.core.callbacks.checkpoint module

class dlk.core.callbacks.checkpoint.CheckpointCallback(config: dlk.core.callbacks.checkpoint.CheckpointCallbackConfig)

Bases: object

Save checkpoint decided by config

class dlk.core.callbacks.checkpoint.CheckpointCallbackConfig(config: Dict)

Bases: object

Config for CheckpointCallback

Config Example:

>>> {
>>>     // default checkpoint configure
>>>     "_name": "checkpoint",
>>>     "config": {
>>>         "monitor": "*@*",    // monitor which metrics or log value
>>>         "save_top_k": 3,   //save top k
>>>         "mode": "*@*", //"max" or "min" select topk min or max checkpoint, min for loss, max for acc
>>>         "save_last": true,  //  always save last checkpoint
>>>         "auto_insert_metric_name": true, //the save file name with or not metric name
>>>         "every_n_train_steps": null, // Number of training steps between checkpoints.
>>>         "every_n_epochs": 1, //Number of epochs between checkpoints.
>>>         "save_on_train_epoch_end": false,// Whether to run checkpointing at the end of the training epoch. If this is False, then the check runs at the end of the validation.
>>>         "save_weights_only": false, //whether save other status like optimizer, etc.
>>>     }
>>> }

dlk.core.callbacks.early_stop module

class dlk.core.callbacks.early_stop.EarlyStoppingCallback(config: dlk.core.callbacks.early_stop.EarlyStoppingCallbackConfig)

Bases: object

Early stop decided by config

class dlk.core.callbacks.early_stop.EarlyStoppingCallbackConfig(config: Dict)

Bases: object

Config for EarlyStoppingCallback

Config Example:

>>> {
>>>     "_name": "early_stop",
>>>     "config":{
>>>         "monitor": "val_loss",
>>>         "mode": "*@*", // min or max, min for the monitor is loss, max for the monitor is acc, f1, etc.
>>>         "patience": 3,
>>>         "min_delta": 0.0,
>>>         "check_on_train_epoch_end": null,
>>>         "strict": true, // if the monitor is not right, raise error
>>>         "stopping_threshold": null, // float, if the value is good enough, stop
>>>         "divergence_threshold": null, // float,  if the value is so bad, stop
>>>         "verbose": true, //verbose mode print more info
>>>     }
>>> }

dlk.core.callbacks.lr_monitor module

class dlk.core.callbacks.lr_monitor.LearningRateMonitorCallback(config: dlk.core.callbacks.lr_monitor.LearningRateMonitorCallbackConfig)

Bases: object

Monitor the learning rate

class dlk.core.callbacks.lr_monitor.LearningRateMonitorCallbackConfig(config: Dict)

Bases: object

Config for LearningRateMonitorCallback

Config Example:

>>> {
>>>     "_name": "lr_monitor",
>>>     "config": {
>>>         "logging_interval": null, // set to None to log at individual interval according to the interval key of each scheduler. other value : step, epoch
>>>         "log_momentum": true, // log momentum or not
>>>     }
>>> }

dlk.core.callbacks.weight_average module

class dlk.core.callbacks.weight_average.StochasticWeightAveragingCallback(config: dlk.core.callbacks.weight_average.StochasticWeightAveragingCallbackConfig)

Bases: object

Average weight by config

class dlk.core.callbacks.weight_average.StochasticWeightAveragingCallbackConfig(config)

Bases: object

Config for StochasticWeightAveragingCallback

Config Example:

>>> {   //weight_average default
>>>     "_name": "weight_average",
>>>     "config": {
>>>         "swa_epoch_start": 0.8, // swa start epoch
>>>         "swa_lrs": null,
>>>             //None. Use the current learning rate of the optimizer at the time the SWA procedure starts.
>>>             //float. Use this value for all parameter groups of the optimizer.
>>>             //List[float]. A list values for each parameter group of the optimizer.
>>>         "annealing_epochs": 10,
>>>         "annealing_strategy": 'cos',
>>>         "device": null, // save device, null for auto detach, if the gpu is oom, you should change this to 'cpu'
>>>     }
>>> }

Module contents

callbacks

dlk.core.callbacks.import_callbacks(callbacks_dir, namespace)