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6cc8ae1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 | """
Training callbacks: EarlyStopping, ModelCheckpoint, MetricLogger.
"""
import json
import time
from pathlib import Path
from typing import Optional
import torch
import torch.nn as nn
class EarlyStopping:
"""Stop training when monitored metric stops improving."""
def __init__(
self,
patience: int = 7,
min_delta: float = 0.001,
mode: str = 'min',
verbose: bool = True,
):
self.patience = patience
self.min_delta = min_delta
self.mode = mode
self.verbose = verbose
self.counter = 0
self.best_value = None
self.early_stop = False
def __call__(self, value: float) -> bool:
if self.best_value is None:
self.best_value = value
return False
if self.mode == 'min':
improved = value < (self.best_value - self.min_delta)
else:
improved = value > (self.best_value + self.min_delta)
if improved:
self.best_value = value
self.counter = 0
else:
self.counter += 1
if self.verbose:
print(f" EarlyStopping: {self.counter}/{self.patience}")
if self.counter >= self.patience:
self.early_stop = True
if self.verbose:
print(" EarlyStopping triggered!")
return True
return False
class ModelCheckpoint:
"""Save model checkpoint when monitored metric improves."""
def __init__(
self,
save_dir: str | Path,
model_name: str = 'model',
mode: str = 'min',
verbose: bool = True,
):
self.save_dir = Path(save_dir)
self.save_dir.mkdir(parents=True, exist_ok=True)
self.model_name = model_name
self.mode = mode
self.verbose = verbose
self.best_value = None
self.best_path = None
def __call__(
self,
value: float,
model: nn.Module,
optimizer: torch.optim.Optimizer = None,
epoch: int = 0,
extra_info: Optional[dict] = None,
) -> bool:
if self.best_value is None:
improved = True
elif self.mode == 'min':
improved = value < self.best_value
else:
improved = value > self.best_value
if improved:
self.best_value = value
filename = f"{self.model_name}_best.pth"
self.best_path = self.save_dir / filename
checkpoint = {
'epoch': epoch,
'model_state_dict': model.state_dict(),
'best_value': self.best_value,
'model_name': self.model_name,
}
if optimizer is not None:
checkpoint['optimizer_state_dict'] = optimizer.state_dict()
if extra_info:
checkpoint.update(extra_info)
torch.save(checkpoint, self.best_path)
if self.verbose:
print(f" Checkpoint saved: {filename} (value: {value:.4f})")
return True
return False
class MetricLogger:
"""Log and store metrics across epochs."""
def __init__(self, log_dir: str | Path = None, model_name: str = 'model'):
self.model_name = model_name
self.log_dir = Path(log_dir) if log_dir else None
if self.log_dir:
self.log_dir.mkdir(parents=True, exist_ok=True)
self.history = {
'train_loss': [],
'train_accuracy': [],
'val_loss': [],
'val_accuracy': [],
'learning_rate': [],
'epoch_time': [],
}
self.start_time = time.time()
def log_epoch(
self,
epoch: int,
train_metrics: dict,
val_metrics: dict,
learning_rate: float,
) -> None:
"""Log metrics for one epoch."""
self.history['train_loss'].append(train_metrics['loss'])
self.history['train_accuracy'].append(train_metrics['accuracy'])
self.history['val_loss'].append(val_metrics['loss'])
self.history['val_accuracy'].append(val_metrics['accuracy'])
self.history['learning_rate'].append(learning_rate)
self.history['epoch_time'].append(train_metrics.get('time_seconds', 0))
print(f" Epoch {epoch:>3d} | "
f"Train Loss: {train_metrics['loss']:.4f} | "
f"Train Acc: {train_metrics['accuracy']:.4f} | "
f"Val Loss: {val_metrics['loss']:.4f} | "
f"Val Acc: {val_metrics['accuracy']:.4f} | "
f"LR: {learning_rate:.6f}")
def save(self) -> None:
"""Save training history to JSON."""
if self.log_dir:
total_time = time.time() - self.start_time
summary = {
'model_name': self.model_name,
'total_training_time_seconds': round(total_time, 2),
'num_epochs': len(self.history['train_loss']),
'best_val_loss': min(self.history['val_loss']) if self.history['val_loss'] else None,
'best_val_accuracy': max(self.history['val_accuracy']) if self.history['val_accuracy'] else None,
'history': self.history,
}
path = self.log_dir / f'{self.model_name}_training_history.json'
with open(path, 'w') as f:
json.dump(summary, f, indent=2)
print(f" Training history saved to {path}")
def get_summary(self) -> dict:
"""Get training summary."""
return {
'model_name': self.model_name,
'total_epochs': len(self.history['train_loss']),
'best_val_loss': min(self.history['val_loss']) if self.history['val_loss'] else None,
'best_val_accuracy': max(self.history['val_accuracy']) if self.history['val_accuracy'] else None,
'final_train_loss': self.history['train_loss'][-1] if self.history['train_loss'] else None,
'final_val_loss': self.history['val_loss'][-1] if self.history['val_loss'] else None,
}
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