From 9ae5fa1a88899180f88ddb14d4cef457ceb847e5 Mon Sep 17 00:00:00 2001
From: Gustaf Rydholm <gustaf.rydholm@gmail.com>
Date: Mon, 5 Apr 2021 20:47:55 +0200
Subject: Add new training loop with PyTorch Lightning, remove stale files

---
 training/trainer/callbacks/early_stopping.py | 108 ---------------------------
 1 file changed, 108 deletions(-)
 delete mode 100644 training/trainer/callbacks/early_stopping.py

(limited to 'training/trainer/callbacks/early_stopping.py')

diff --git a/training/trainer/callbacks/early_stopping.py b/training/trainer/callbacks/early_stopping.py
deleted file mode 100644
index 02b431f..0000000
--- a/training/trainer/callbacks/early_stopping.py
+++ /dev/null
@@ -1,108 +0,0 @@
-"""Implements Early stopping for PyTorch model."""
-from typing import Dict, Union
-
-from loguru import logger
-import numpy as np
-import torch
-from torch import Tensor
-from training.trainer.callbacks import Callback
-
-
-class EarlyStopping(Callback):
-    """Stops training when a monitored metric stops improving."""
-
-    mode_dict = {
-        "min": torch.lt,
-        "max": torch.gt,
-    }
-
-    def __init__(
-        self,
-        monitor: str = "val_loss",
-        min_delta: float = 0.0,
-        patience: int = 3,
-        mode: str = "auto",
-    ) -> None:
-        """Initializes the EarlyStopping callback.
-
-        Args:
-            monitor (str): Description of parameter `monitor`. Defaults to "val_loss".
-            min_delta (float): Description of parameter `min_delta`. Defaults to 0.0.
-            patience (int): Description of parameter `patience`. Defaults to 3.
-            mode (str): Description of parameter `mode`. Defaults to "auto".
-
-        """
-        super().__init__()
-        self.monitor = monitor
-        self.patience = patience
-        self.min_delta = torch.tensor(min_delta)
-        self.mode = mode
-        self.wait_count = 0
-        self.stopped_epoch = 0
-
-        if mode not in ["auto", "min", "max"]:
-            logger.warning(
-                f"EarlyStopping 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"EarlyStopping mode set to {self.mode} for monitoring {self.monitor}."
-            )
-
-        self.torch_inf = torch.tensor(np.inf)
-        self.min_delta *= 1 if self.monitor_op == torch.gt else -1
-        self.best_score = (
-            self.torch_inf if self.monitor_op == torch.lt else -self.torch_inf
-        )
-
-    @property
-    def monitor_op(self) -> float:
-        """Returns the comparison method."""
-        return self.mode_dict[self.mode]
-
-    def on_fit_begin(self) -> Union[torch.lt, torch.gt]:
-        """Reset the early stopping variables for reuse."""
-        self.wait_count = 0
-        self.stopped_epoch = 0
-        self.best_score = (
-            self.torch_inf if self.monitor_op == torch.lt else -self.torch_inf
-        )
-
-    def on_epoch_end(self, epoch: int, logs: Dict) -> None:
-        """Computes the early stop criterion."""
-        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
-            self.wait_count = 0
-        else:
-            self.wait_count += 1
-            if self.wait_count >= self.patience:
-                self.stopped_epoch = epoch
-                self.model.stop_training = True
-
-    def on_fit_end(self) -> None:
-        """Logs if early stopping was used."""
-        if self.stopped_epoch > 0:
-            logger.info(
-                f"Stopped training at epoch {self.stopped_epoch + 1} with early stopping."
-            )
-
-    def get_monitor_value(self, logs: Dict) -> Union[Tensor, None]:
-        """Extracts the monitor value."""
-        monitor_value = logs.get(self.monitor)
-        if monitor_value is None:
-            logger.warning(
-                f"Early stopping is conditioned on metric {self.monitor} which is not available. Available"
-                + f"metrics are: {','.join(list(logs.keys()))}"
-            )
-            return None
-        return torch.tensor(monitor_value)
-- 
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