BaseImageLogger

class easypl.callbacks.loggers.base_image.BaseImageLogger(phase: str = 'train', max_samples: int = 1, class_names: Optional[List] = None, mode: str = 'first', sample_key: Optional = None, score_func: Optional[Callable] = None, largest: bool = True, dir_path: Optional[str] = None, save_on_disk: bool = False)

Abstract callback class for logging images

phase

Phase which will be used by this Logger. Available: [“train”, “val”, “test”, “predict”].

Type

str

max_samples

Maximum number of samples which will be logged at one epoch.

Type

int

class_names

List of class names for pretty logging. If None, then class_names will set range of number of classes.

Type

Optional[List]

mode

Mode of sample generation. Available modes: [“random”, “first”, “top”].

Type

str

sample_key

Key of batch, which define sample. If None, then sample_key will parse learner.data_keys.

Type

Optional

score_func

Function for score evaluation. Necessary if “mode” = “top”.

Type

Optional[Callable]

largest

Sorting order for “top” mode

Type

bool

dir_path

If defined, then logs will be writed in this directory. Else in lighting_logs.

Type

Optional[str]

save_on_disk

If true, then logs will be writed on disk to “dir_path”.

Type

bool

_post_init(trainer: Trainer, pl_module: LightningModule)

Method for initialization inverse transforms.

trainer

Trainer of pytorch-lightning

Type

pytorch_lightning.Trainer

pl_module

LightningModule of pytorch-lightning

Type

pytorch_lightning.LightningModule