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fab18b7 | 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 | """
GAN Trainer – handles the full training loop.
Usage:
from src.training.trainer import Trainer
trainer = Trainer(config)
trainer.train()
"""
from __future__ import annotations
import time
from pathlib import Path
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from omegaconf import DictConfig
from src.models import Generator, Discriminator
from src.training.losses import GANLoss
from src.utils.checkpoint import save_checkpoint, load_checkpoint
from src.utils.visualization import save_sample_grid
class Trainer:
def __init__(self, cfg: DictConfig):
self.cfg = cfg
self.device = self._get_device()
# ── Models ──────────────────────────────────────────
self.G = Generator(
latent_dim=cfg.model.latent_dim,
features=cfg.model.generator_features,
channels=cfg.model.channels,
image_size=cfg.model.image_size,
).to(self.device)
self.D = Discriminator(
features=cfg.model.discriminator_features,
channels=cfg.model.channels,
image_size=cfg.model.image_size,
).to(self.device)
# ── Optimisers ──────────────────────────────────────
self.opt_G = torch.optim.Adam(
self.G.parameters(),
lr=cfg.training.lr_generator,
betas=(cfg.training.beta1, cfg.training.beta2),
)
self.opt_D = torch.optim.Adam(
self.D.parameters(),
lr=cfg.training.lr_discriminator,
betas=(cfg.training.beta1, cfg.training.beta2),
)
# ── Loss ────────────────────────────────────────────
self.criterion = GANLoss(
label_smoothing=cfg.training.label_smoothing,
device=self.device,
)
# Fixed noise for consistent sample grid across epochs
self.fixed_noise = torch.randn(
cfg.logging.sample_count,
cfg.model.latent_dim,
device=self.device,
)
# Directories
self.log_dir = Path(cfg.logging.log_dir)
self.ckpt_dir = Path(cfg.logging.checkpoint_dir)
self.log_dir.mkdir(parents=True, exist_ok=True)
self.ckpt_dir.mkdir(parents=True, exist_ok=True)
self.start_epoch = 0
self.history: list[dict] = []
# ────────────────────────────────────────────────────────
# Public API
# ────────────────────────────────────────────────────────
def train(self, loader: DataLoader, resume: str | None = None) -> None:
if resume:
self.start_epoch = load_checkpoint(
resume, self.G, self.D, self.opt_G, self.opt_D, self.device
)
print(f"[Trainer] Resumed from epoch {self.start_epoch}")
total_epochs = self.cfg.training.epochs
print(f"[Trainer] Starting training for {total_epochs} epochs on {self.device}")
for epoch in range(self.start_epoch, total_epochs):
t0 = time.time()
stats = self._train_epoch(loader, epoch)
elapsed = time.time() - t0
self.history.append(stats)
print(
f"Epoch [{epoch+1:>4}/{total_epochs}] "
f"D: {stats['d_loss']:.4f} G: {stats['g_loss']:.4f} "
f"time: {elapsed:.1f}s"
)
# Save sample grid
if (epoch + 1) % self.cfg.logging.sample_interval == 0:
save_sample_grid(
self.G, self.fixed_noise,
path=self.log_dir / f"samples_epoch_{epoch+1:04d}.png",
nrow=4,
)
# Save checkpoint
if (epoch + 1) % self.cfg.logging.save_interval == 0:
save_checkpoint(
self.G, self.D, self.opt_G, self.opt_D,
epoch=epoch + 1,
path=self.ckpt_dir / f"ckpt_epoch_{epoch+1:04d}.pth",
)
# Always save final checkpoint
save_checkpoint(
self.G, self.D, self.opt_G, self.opt_D,
epoch=total_epochs,
path=self.ckpt_dir / "ckpt_final.pth",
)
print("[Trainer] Training complete.")
# ────────────────────────────────────────────────────────
# Internal
# ────────────────────────────────────────────────────────
def _train_epoch(self, loader: DataLoader, epoch: int) -> dict:
self.G.train()
self.D.train()
total_d, total_g, n_batches = 0.0, 0.0, 0
for real_imgs, _ in loader:
real_imgs = real_imgs.to(self.device)
batch = real_imgs.size(0)
# ── Train Discriminator ──────────────────────────
for _ in range(self.cfg.training.n_critic):
noise = torch.randn(batch, self.cfg.model.latent_dim, device=self.device)
fake_imgs = self.G(noise).detach()
real_logits = self.D(real_imgs).squeeze()
fake_logits = self.D(fake_imgs).squeeze()
d_loss, _ = self.criterion.discriminator_loss(real_logits, fake_logits)
self.opt_D.zero_grad()
d_loss.backward()
nn.utils.clip_grad_norm_(self.D.parameters(), max_norm=1.0)
self.opt_D.step()
# ── Train Generator ──────────────────────────────
noise = torch.randn(batch, self.cfg.model.latent_dim, device=self.device)
fake_imgs = self.G(noise)
fake_logits = self.D(fake_imgs).squeeze()
g_loss = self.criterion.generator_loss(fake_logits)
self.opt_G.zero_grad()
g_loss.backward()
nn.utils.clip_grad_norm_(self.G.parameters(), max_norm=1.0)
self.opt_G.step()
total_d += d_loss.item()
total_g += g_loss.item()
n_batches += 1
return {"d_loss": total_d / n_batches, "g_loss": total_g / n_batches, "epoch": epoch + 1}
@staticmethod
def _get_device() -> torch.device:
if torch.cuda.is_available():
device = torch.device("cuda")
elif torch.backends.mps.is_available():
device = torch.device("mps")
else:
device = torch.device("cpu")
print(f"[Trainer] Using device: {device}")
return device
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