Source code for kornia.x.callbacks

from pathlib import Path
from typing import Optional

import torch
import torch.nn as nn

from .utils import TrainerState

[docs]class EarlyStopping: """Callback that evaluates whether there is improvement in the loss function. The module track the losses and in case of finish patience sends a termination signal to the trainer. In case of termination, the module will save the last model. Args: monitor: the name of the value to track. min_delta: the minimum difference between losses to increase the patience counter. patience: the number of times to wait until the trainer does not terminate. filepath: a backup filename to save the file in case of termination. **Usage example:** .. code:: python early_stop = EarlyStopping( monitor="top5", filepath="" ) trainer = ImageClassifierTrainer(..., callbacks={"terminate", early_stop} ) """ def __init__(self, monitor: str, min_delta: float = 0., patience: int = 8) -> None: self.monitor = monitor self.min_delta = min_delta self.patience = patience self.counter: int = 0 self.best_score: Optional[float] = None self.early_stop: bool = False def __call__(self, model: nn.Module, epoch: int, valid_metric) -> TrainerState: score: float = -valid_metric[self.monitor].avg # TODO: rethink about this logic - doesn't seem to do the job. if self.best_score is None: self.best_score = score elif score < self.best_score + self.min_delta: self.counter += 1 if self.counter >= self.patience: self.early_stop = True else: self.best_score = score self.counter = 0 if self.early_stop: # TODO: figure out later how and where to save # store old metric and save new model #, self.filepath) print(f"[INFO] Early-Stopping the training process. Epoch: {epoch}.") return TrainerState.TERMINATE return TrainerState.TRAINING
[docs]class ModelCheckpoint: """Callback that save the model at the end of everyepoch. Args: filepath: the where to save the mode. monitor: the name of the value to track. **Usage example:** .. code:: python model_checkpoint = ModelCheckpoint( filepath="./outputs", monitor="top5", ) trainer = ImageClassifierTrainer(..., callbacks={"checkpoint", model_checkpoint} ) """ def __init__(self, filepath: str, monitor: str) -> None: self.filepath = filepath self.monitor = monitor # track best model self.best_metric: float = 0. # create directory Path(self.filepath).mkdir(parents=True, exist_ok=True) def __call__(self, model: nn.Module, epoch: int, valid_metric) -> None: valid_metric_value: float = valid_metric[self.monitor].avg if valid_metric_value > self.best_metric: self.best_metric = valid_metric_value # store old metric and save new model filename = Path(self.filepath) / f"model_{epoch}.pt", filename) ...