1 files changed, 0 insertions, 95 deletions
diff --git a/training/trainer/callbacks/checkpoint.py b/training/trainer/callbacks/checkpoint.py
deleted file mode 100644
index a54e0a9..0000000
--- a/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