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+"""Metaclass for callback functions."""
+
+from enum import Enum
+from typing import Callable, Dict, List, Optional, Type, Union
+
+from loguru import logger
+import numpy as np
+import torch
+
+from text_recognizer.models import Model
+
+
+class ModeKeys:
+ """Mode keys for CallbackList."""
+
+ TRAIN = "train"
+ VALIDATION = "validation"
+
+
+class Callback:
+ """Metaclass for callbacks used in training."""
+
+ def __init__(self) -> None:
+ """Initializes the Callback instance."""
+ self.model = None
+
+ def set_model(self, model: Type[Model]) -> None:
+ """Set the model."""
+ self.model = model
+
+ def on_fit_begin(self) -> None:
+ """Called when fit begins."""
+ pass
+
+ def on_fit_end(self) -> None:
+ """Called when fit ends."""
+ pass
+
+ def on_epoch_begin(self, epoch: int, logs: Optional[Dict] = None) -> None:
+ """Called at the beginning of an epoch. Only used in training mode."""
+ pass
+
+ def on_epoch_end(self, epoch: int, logs: Optional[Dict] = None) -> None:
+ """Called at the end of an epoch. Only used in training mode."""
+ pass
+
+ def on_train_batch_begin(self, batch: int, logs: Optional[Dict] = None) -> None:
+ """Called at the beginning of an epoch."""
+ pass
+
+ def on_train_batch_end(self, batch: int, logs: Optional[Dict] = None) -> None:
+ """Called at the end of an epoch."""
+ pass
+
+ def on_validation_batch_begin(
+ self, batch: int, logs: Optional[Dict] = None
+ ) -> None:
+ """Called at the beginning of an epoch."""
+ pass
+
+ def on_validation_batch_end(self, batch: int, logs: Optional[Dict] = None) -> None:
+ """Called at the end of an epoch."""
+ pass
+
+
+class CallbackList:
+ """Container for abstracting away callback calls."""
+
+ mode_keys = ModeKeys()
+
+ def __init__(self, model: Type[Model], callbacks: List[Callback] = None) -> None:
+ """Container for `Callback` instances.
+
+ This object wraps a list of `Callback` instances and allows them all to be
+ called via a single end point.
+
+ Args:
+ model (Type[Model]): A `Model` instance.
+ callbacks (List[Callback]): List of `Callback` instances. Defaults to None.
+
+ """
+
+ self._callbacks = callbacks or []
+ if model:
+ self.set_model(model)
+
+ def set_model(self, model: Type[Model]) -> None:
+ """Set the model for all callbacks."""
+ self.model = model
+ for callback in self._callbacks:
+ callback.set_model(model=self.model)
+
+ def append(self, callback: Type[Callback]) -> None:
+ """Append new callback to callback list."""
+ self.callbacks.append(callback)
+
+ def on_fit_begin(self) -> None:
+ """Called when fit begins."""
+ for callback in self._callbacks:
+ callback.on_fit_begin()
+
+ def on_fit_end(self) -> None:
+ """Called when fit ends."""
+ for callback in self._callbacks:
+ callback.on_fit_end()
+
+ def on_epoch_begin(self, epoch: int, logs: Optional[Dict] = None) -> None:
+ """Called at the beginning of an epoch."""
+ for callback in self._callbacks:
+ callback.on_epoch_begin(epoch, logs)
+
+ def on_epoch_end(self, epoch: int, logs: Optional[Dict] = None) -> None:
+ """Called at the end of an epoch."""
+ for callback in self._callbacks:
+ callback.on_epoch_end(epoch, logs)
+
+ def _call_batch_hook(
+ self, mode: str, hook: str, batch: int, logs: Optional[Dict] = None
+ ) -> None:
+ """Helper function for all batch_{begin | end} methods."""
+ if hook == "begin":
+ self._call_batch_begin_hook(mode, batch, logs)
+ elif hook == "end":
+ self._call_batch_end_hook(mode, batch, logs)
+ else:
+ raise ValueError(f"Unrecognized hook {hook}.")
+
+ def _call_batch_begin_hook(
+ self, mode: str, batch: int, logs: Optional[Dict] = None
+ ) -> None:
+ """Helper function for all `on_*_batch_begin` methods."""
+ hook_name = f"on_{mode}_batch_begin"
+ self._call_batch_hook_helper(hook_name, batch, logs)
+
+ def _call_batch_end_hook(
+ self, mode: str, batch: int, logs: Optional[Dict] = None
+ ) -> None:
+ """Helper function for all `on_*_batch_end` methods."""
+ hook_name = f"on_{mode}_batch_end"
+ self._call_batch_hook_helper(hook_name, batch, logs)
+
+ def _call_batch_hook_helper(
+ self, hook_name: str, batch: int, logs: Optional[Dict] = None
+ ) -> None:
+ """Helper function for `on_*_batch_begin` methods."""
+ for callback in self._callbacks:
+ hook = getattr(callback, hook_name)
+ hook(batch, logs)
+
+ def on_train_batch_begin(self, batch: int, logs: Optional[Dict] = None) -> None:
+ """Called at the beginning of an epoch."""
+ self._call_batch_hook(self.mode_keys.TRAIN, "begin", batch, logs)
+
+ def on_train_batch_end(self, batch: int, logs: Optional[Dict] = None) -> None:
+ """Called at the end of an epoch."""
+ self._call_batch_hook(self.mode_keys.TRAIN, "end", batch, logs)
+
+ def on_validation_batch_begin(
+ self, batch: int, logs: Optional[Dict] = None
+ ) -> None:
+ """Called at the beginning of an epoch."""
+ self._call_batch_hook(self.mode_keys.VALIDATION, "begin", batch, logs)
+
+ def on_validation_batch_end(self, batch: int, logs: Optional[Dict] = None) -> None:
+ """Called at the end of an epoch."""
+ self._call_batch_hook(self.mode_keys.VALIDATION, "end", batch, logs)
+
+ def __iter__(self) -> iter:
+ """Iter function for callback list."""
+ return iter(self._callbacks)
+
+
+class Checkpoint(Callback):
+ """Saving model parameters at the end of each epoch."""
+
+ mode_dict = {
+ "min": torch.lt,
+ "max": torch.gt,
+ }
+
+ def __init__(
+ self, monitor: str = "accuracy", mode: str = "auto", min_delta: float = 0.0
+ ) -> None:
+ """Monitors a quantity that will allow us to determine the best model weights.
+
+ Args:
+ monitor (str): Name of the quantity to monitor. Defaults to "accuracy".
+ mode (str): Description of parameter `mode`. Defaults to "auto".
+ min_delta (float): Description of parameter `min_delta`. Defaults to 0.0.
+
+ """
+ super().__init__()
+ self.monitor = monitor
+ self.mode = mode
+ self.min_delta = torch.tensor(min_delta)
+
+ if mode not in ["auto", "min", "max"]:
+ logger.warning(f"Checkpoint mode {mode} is unkown, fallback to auto mode.")
+
+ self.mode = "auto"
+
+ if self.mode == "auto":
+ if "accuracy" in self.monitor:
+ self.mode = "max"
+ else:
+ self.mode = "min"
+ logger.debug(
+ f"Checkpoint mode set to {self.mode} for monitoring {self.monitor}."
+ )
+
+ torch_inf = torch.tensor(np.inf)
+ self.min_delta *= 1 if self.monitor_op == torch.gt else -1
+ self.best_score = torch_inf if self.monitor_op == torch.lt else -torch_inf
+
+ @property
+ def monitor_op(self) -> float:
+ """Returns the comparison method."""
+ return self.mode_dict[self.mode]
+
+ def on_epoch_end(self, epoch: int, logs: Dict) -> None:
+ """Saves a checkpoint for the network parameters.
+
+ Args:
+ epoch (int): The current epoch.
+ logs (Dict): The log containing the monitored metrics.
+
+ """
+ current = self.get_monitor_value(logs)
+ if current is None:
+ return
+ if self.monitor_op(current - self.min_delta, self.best_score):
+ self.best_score = current
+ is_best = True
+ else:
+ is_best = False
+
+ self.model.save_checkpoint(is_best, epoch, self.monitor)
+
+ def get_monitor_value(self, logs: Dict) -> Union[float, None]:
+ """Extracts the monitored value."""
+ monitor_value = logs.get(self.monitor)
+ if monitor_value is None:
+ logger.warning(
+ f"Checkpoint is conditioned on metric {self.monitor} which is not available. Available"
+ + f"metrics are: {','.join(list(logs.keys()))}"
+ )
+ return None
+ return monitor_value
generated by cgit v1.2.3-70-g09d2 (git 2.46.0) at 2024-09-20 12:42:33 +0000