File size: 11,850 Bytes
5d2fa0b | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 | import torch
import wandb
from omegaconf import OmegaConf
from timm.utils import ModelEmaV2
from torch import nn
from torch.amp import GradScaler, autocast
from torchvision.transforms import v2
from tqdm import tqdm
from .metrics import MetricTracker
from .utils import EarlyStopping, save_checkpoint
class Trainer:
def __init__(
self,
model,
train_loader,
val_loader,
criterion,
optimizer,
scheduler,
config,
device,
):
self.model = model
self.train_loader = train_loader
self.val_loader = val_loader
self.criterion = criterion
self.optimizer = optimizer
self.scheduler = scheduler
self.config = config
self.device = device
self.early_stopping = EarlyStopping(
patience=config.training.early_stopping_patience, mode="max"
)
self.scaler = GradScaler(device.type, enabled=config.training.mixed_precision)
self.use_ema = (
getattr(config.training, "ema", None) and config.training.ema.enabled
)
if self.use_ema:
ema_decay = getattr(config.training.ema, "decay", 0.9999)
self.model_ema = ModelEmaV2(self.model, decay=ema_decay, device=device)
else:
self.model_ema = None
self.num_classes = config.model.num_classes
self.use_mixup = False
if config.augmentation.prob > 0:
self.use_mixup = True
cutmix = v2.CutMix(
alpha=config.augmentation.cutmix_alpha, num_classes=self.num_classes
)
mixup = v2.MixUp(
alpha=config.augmentation.mixup_alpha, num_classes=self.num_classes
)
self.cutmix_or_mixup = v2.RandomChoice([cutmix, mixup])
self.train_metrics = MetricTracker(num_classes=self.num_classes, device=device)
self.val_metrics = MetricTracker(num_classes=self.num_classes, device=device)
if self.use_ema:
self.val_ema_metrics = MetricTracker(
num_classes=self.num_classes, device=device
)
def train_one_epoch(self, epoch):
self.model.train()
if self.config.model.freeze_bn:
for module in self.model.modules():
if isinstance(module, (nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d)):
module.eval()
self.train_metrics.reset()
pbar = tqdm(self.train_loader, desc=f"Epoch {epoch} [Train]")
for batch_idx, (images, targets) in enumerate(pbar):
images, targets = images.to(self.device), targets.to(self.device)
is_mixed = False
# apply MixUp or CutMix
if self.use_mixup and torch.rand(1).item() < self.config.augmentation.prob:
images, targets = self.cutmix_or_mixup(images, targets)
is_mixed = True
if targets.ndim == 1:
targets = torch.nn.functional.one_hot(
targets, num_classes=self.num_classes
).float()
with autocast(
device_type=self.device.type,
enabled=self.config.training.mixed_precision,
):
outputs = self.model(images)
loss = self.criterion(outputs, targets)
# gradient accumulation normalizer
loss = loss / self.config.training.gradient_accumulation_steps
self.scaler.scale(loss).backward()
if (batch_idx + 1) % self.config.training.gradient_accumulation_steps == 0:
if self.config.training.clip_grad_norm > 0:
self.scaler.unscale_(self.optimizer)
torch.nn.utils.clip_grad_norm_(
self.model.parameters(), self.config.training.clip_grad_norm
)
self.scaler.step(self.optimizer)
self.scaler.update()
self.optimizer.zero_grad()
if self.config.scheduler.name == "cosine_warmup":
self.scheduler.step()
if self.use_ema:
self.model_ema.update(self.model)
batch_loss = loss.item() * self.config.training.gradient_accumulation_steps
self.train_metrics.update(
outputs.detach(),
targets.detach(),
loss=batch_loss,
skip_metrics=is_mixed,
)
pbar.set_postfix({"loss": f"{batch_loss:.4f}"})
if self.config.logging.use_wandb:
wandb.log({"train/batch_loss": batch_loss})
if (batch_idx + 1) % self.config.training.gradient_accumulation_steps != 0:
if self.config.training.clip_grad_norm > 0:
self.scaler.unscale_(self.optimizer)
torch.nn.utils.clip_grad_norm_(
self.model.parameters(), self.config.training.clip_grad_norm
)
self.scaler.step(self.optimizer)
self.scaler.update()
self.optimizer.zero_grad()
if self.config.scheduler.name == "cosine_warmup":
self.scheduler.step()
if self.use_ema:
self.model_ema.update(self.model)
metrics = self.train_metrics.compute()
# Step schedulers that step per epoch
if self.config.scheduler.name == "step":
self.scheduler.step()
elif self.config.scheduler.name == "cosine":
self.scheduler.step()
return metrics
def validate(self, epoch):
self.model.eval()
self.val_metrics.reset()
if self.use_ema:
self.model_ema.module.eval()
self.val_ema_metrics.reset()
pbar = tqdm(self.val_loader, desc=f"Epoch {epoch} [Val]")
with torch.no_grad():
for images, targets in pbar:
images, targets = images.to(self.device), targets.to(self.device)
if targets.ndim == 1:
targets = torch.nn.functional.one_hot(
targets, num_classes=self.num_classes
).float()
with autocast(
device_type=self.device.type,
enabled=self.config.training.mixed_precision,
):
outputs = self.model(images)
loss = self.criterion(outputs, targets)
if self.use_ema:
ema_outputs = self.model_ema.module(images)
ema_loss = self.criterion(ema_outputs, targets)
self.val_metrics.update(
outputs.detach(), targets.detach(), loss=loss.detach()
)
if self.use_ema:
self.val_ema_metrics.update(
ema_outputs.detach(), targets.detach(), loss=ema_loss.detach()
)
pbar.set_postfix(
{
"loss": f"{loss.item():.4f}",
"ema_loss": f"{ema_loss.item():.4f}",
}
)
else:
pbar.set_postfix({"loss": f"{loss.item():.4f}"})
metrics = {"current": self.val_metrics.compute()}
if self.use_ema:
metrics["ema"] = self.val_ema_metrics.compute()
primary_map = metrics[self.config.training.ema.eval_mode]["mAP"]
if self.config.scheduler.name == "plateau":
self.scheduler.step(primary_map)
return metrics
def fit(self, start_epoch=1):
best_map = 0.0
for epoch in range(start_epoch, self.config.training.epochs + 1):
train_metrics = self.train_one_epoch(epoch)
val_metrics = self.validate(epoch)
lrs = [pg["lr"] for pg in self.optimizer.param_groups]
log_dict = {
"train/loss": train_metrics["loss"],
"train/mAP": train_metrics["mAP"],
"train/accuracy": train_metrics["accuracy"],
"lr/backbone": lrs[0],
"lr/head": lrs[1],
"epoch": epoch,
}
if self.use_ema:
log_dict.update(
{
"val/loss": val_metrics["current"]["loss"],
"val/mAP": val_metrics["current"]["mAP"],
"val/accuracy": val_metrics["current"]["accuracy"],
"val/ema_loss": val_metrics["ema"]["loss"],
"val/ema_mAP": val_metrics["ema"]["mAP"],
"val/ema_accuracy": val_metrics["ema"]["accuracy"],
}
)
else:
log_dict.update(
{
"val/loss": val_metrics["current"]["loss"],
"val/mAP": val_metrics["current"]["mAP"],
"val/accuracy": val_metrics["current"]["accuracy"],
}
)
if self.config.logging.use_wandb:
wandb.log(log_dict)
print(f"\nEpoch {epoch} Summary:")
print(f"LR: Backbone: {lrs[0]:.2e} | Head: {lrs[1]:.2e}")
print(
f"Train - Loss: {train_metrics['loss']:.4f}, mAP: {train_metrics['mAP']:.4f}, Acc: {train_metrics['accuracy']:.4f}"
)
if self.use_ema:
print(
f"Val (Current) - Loss: {val_metrics['current']['loss']:.4f}, mAP: {val_metrics['current']['mAP']:.4f}, Acc: {val_metrics['current']['accuracy']:.4f}"
)
print(
f"Val (EMA) - Loss: {val_metrics['ema']['loss']:.4f}, mAP: {val_metrics['ema']['mAP']:.4f}, Acc: {val_metrics['ema']['accuracy']:.4f}"
)
else:
print(
f"Val - Loss: {val_metrics['current']['loss']:.4f}, mAP: {val_metrics['current']['mAP']:.4f}, Acc: {val_metrics['current']['accuracy']:.4f}"
)
primary_map = val_metrics[self.config.training.ema.eval_mode]["mAP"]
is_best = self.early_stopping(primary_map)
if is_best:
best_map = primary_map
print(f"Epoch {epoch} is the new best model. mAP: {best_map:.4f}")
# Checkpointing
state = {
"epoch": epoch,
"state_dict": self.model.state_dict(),
"state_dict_ema": self.model_ema.module.state_dict()
if self.use_ema
else None,
"optimizer": self.optimizer.state_dict(),
"scheduler": self.scheduler.state_dict() if self.scheduler else None,
"scaler": self.scaler.state_dict(),
"early_stopping": {
"best_score": self.early_stopping.best_score,
"counter": self.early_stopping.counter,
"early_stop": self.early_stopping.early_stop,
},
"rng_states": {
"torch": torch.get_rng_state(),
"cuda": torch.cuda.get_rng_state_all()
if torch.cuda.is_available()
else None,
},
"val_mAP": primary_map,
"config": OmegaConf.to_yaml(self.config),
"wandb_run_id": wandb.run.id if wandb.run is not None else None,
}
save_checkpoint(state, is_best, self.config.logging.checkpoint_dir)
if self.early_stopping.early_stop:
print(f"Early stopping triggered at epoch {epoch}")
break
print("Training complete!")
|