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-rw-r--r--src/training/trainer/callbacks/checkpoint.py95
1 files changed, 0 insertions, 95 deletions
diff --git a/src/training/trainer/callbacks/checkpoint.py b/src/training/trainer/callbacks/checkpoint.py
deleted file mode 100644
index a54e0a9..0000000
--- a/src/training/trainer/callbacks/checkpoint.py
+++ /dev/null
@@ -1,95 +0,0 @@
-"""Callback checkpoint for training models."""
-from enum import Enum
-from pathlib import Path
-from typing import Callable, Dict, List, Optional, Type, Union
-
-from loguru import logger
-import numpy as np
-import torch
-from training.trainer.callbacks import Callback
-
-from text_recognizer.models import Model
-
-
-class Checkpoint(Callback):
- """Saving model parameters at the end of each epoch."""
-
- mode_dict = {
- "min": torch.lt,
- "max": torch.gt,
- }
-
- def __init__(
- self,
- checkpoint_path: Union[str, Path],
- 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:
- checkpoint_path (Union[str, Path]): Path to the experiment with the checkpoint.
- 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.checkpoint_path = Path(checkpoint_path)
- 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(self.checkpoint_path, 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