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99ec8a2 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 | import torch
import torch.nn as nn
import torch.optim as optim
from tqdm import tqdm
from typing import List, Any, Optional, Tuple, Dict
# mine
from data.dataloader import DataLoader
class EPUTrainer:
def __init__(self,
model: nn.Module,
device: torch.device,
optimizer: optim.Optimizer,
criterion: nn.Module,
epochs: int,
train_loader: DataLoader,
val_loader: Optional[DataLoader] = None,
callbacks: Optional[List[object]] = None,
metrics: Optional = None,
checkpoint_dir: Optional[str] = None,
):
self.model = model
self.val_loader = val_loader
self.train_loader = train_loader
self.device = device
self.epochs = epochs
self.optimizer = optimizer
self.criterion = criterion
self.callbacks = callbacks or []
self.checkpoint_dir = checkpoint_dir
self.metrics_fun = metrics
# if self.metrics_fun is None:
# init values
self.best_metric = float("inf")
self.best_model_path = None
self.history = []
self.state = {"model": self.model,
"epoch": 0,
"early_stop": False,
}
def train(self):
self.model.to(self.device)
self._on_training_begin()
for epoch in range(self.epochs):
self.state["epoch"] = epoch
self._on_epoch_begin()
train_loss, train_metrics = self._train_one_epoch()
val_loss, val_metrics = self._validate_epoch()
self.history.append({"epoch": epoch,
"train_loss": train_loss,
"val_loss": val_loss,
"train_metrics": train_metrics,
"val_metrics": val_metrics,}
)
self._on_epoch_end(train_loss, train_metrics, val_loss, val_metrics)
self._on_validation_end()
if self.state.get("early_stop", False):
print("Early stopping triggered.")
break
self._on_training_end()
# self._export_metrics_to_json()
def _train_one_epoch(self) -> Tuple[float, Dict[str, float]]:
self.model.train()
running_loss = 0.0
predictions, ground_truth = [], []
for i, sample in enumerate(tqdm(self.train_loader, desc=f"Training Epoch {self.state['epoch'] + 1}")):
x, y = sample
x = x.to(self.device)
y = y.to(self.device, dtype=torch.float32).unsqueeze(1) # from [bs] to [bs, 1]
self.optimizer.zero_grad()
y_hat = self.model(x, ret_raw_logits=True) # w/o EPU activation -applied internally in loss
loss = self.criterion(y_hat, y)
loss.backward()
self.optimizer.step()
running_loss += loss.item()
predictions.append(y_hat.detach().cpu())
ground_truth.append(y.detach().cpu())
for callback in self.callbacks:
if hasattr(callback, "on_batch_end"):
callback.on_batch_end(
{**self.state,
"batch": i,
"loss": loss.item()}
)
avg_loss = running_loss / len(self.train_loader)
metrics = {}
if self.metrics_fun is not None:
metrics = self.metrics_fun.compute(
y_true=torch.cat(ground_truth, axis=0),
y_pred=torch.cat(predictions, axis=0)
)
return avg_loss, metrics
def _validate_epoch(self) -> Tuple[float, Dict[str, float]]:
if self.val_loader is None:
return 0.0, {}
self.model.eval()
total_loss = 0
predictions, ground_truths = [], []
with torch.no_grad():
for sample in tqdm(self.val_loader, desc="Validating"):
x, y = sample
x = x.to(self.device)
y = y.to(self.device, dtype=torch.float32).unsqueeze(1) # from [bs] to [bs, 1]
y_hat = self.model(x, ret_raw_logits=True)
loss = self.criterion(y_hat, y)
total_loss += loss.item()
predictions.append(y_hat.detach().cpu())
ground_truths.append(y.detach().cpu())
avg_loss = total_loss / len(self.val_loader)
metrics = {}
if self.metrics_fun is not None:
metrics = self.metrics_fun.compute(
y_true=torch.cat(ground_truths, axis=0),
y_pred=torch.cat(predictions, axis=0)
)
return avg_loss, metrics
def _on_training_begin(self):
for callback in self.callbacks:
if hasattr(callback, "on_training_begin"):
callback.on_training_begin(self.state)
def _on_epoch_begin(self):
for callback in self.callbacks:
if hasattr(callback, "on_epoch_begin"):
callback.on_epoch_begin(self.state)
def _on_epoch_end(self, train_loss, train_metrics, val_loss, val_metrics):
# update state
self.state.update(
{"train_loss": train_loss,
"val_loss": val_loss,
"train_metrics": train_metrics,
"val_metrics": val_metrics,
}
)
# print losses
print(f"Epoch {self.state['epoch'] + 1} | "
f"Train loss: {train_loss:.4f} | Validation Loss: {val_loss:.4f}")
# print metrics
if train_metrics is not None:
train_metrics_str = " | ".join([f"{k}: {v:.4f}" for k, v in train_metrics.items()])
print(f"Train metrics:\t\t {train_metrics_str}")
if val_metrics:
val_metrics_str = " | ".join([f"{k}: {v:.4f}" for k, v in val_metrics.items()])
print(f"Validation metrics:\t {val_metrics_str}")
# exec callbacks
for callback in self.callbacks:
if hasattr(callback, "on_epoch_end"):
callback.on_epoch_end(self.state)
def _on_validation_end(self,):
for callback in self.callbacks:
if hasattr(callback, "on_validation_end"):
# print(self.state)
callback.on_validation_end(self.state)
def _on_training_end(self):
for callback in self.callbacks:
if hasattr(callback, "on_training_end"):
callback.on_training_end(self.state)
def get_model(self) -> torch.nn.Module:
return self.model
def get_metrics(self):
return self.metrics_fun
# def _export_metrics_to_json(self):
# if self.checkpoint_dir is not None:
# metrics_path = os.path.join(self.checkpoint_dir, "metrics.json")
# with open(metrics_path, "w") as f:
# json.dump(self.history, f, indent=4)
# print(f"Metrics exported to {metrics_path}")
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