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b8fae22 | 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 | """DDP + AMP training loop.
* DDP: launched via torchrun; uses DistributedSampler. Single-GPU also works.
* AMP: bf16 (A100+) / fp16 (V100, with GradScaler) / fp32.
* Best checkpoint chosen by mean foreground Dice on the val split.
"""
from __future__ import annotations
import os
import math
import json
import time
import numpy as np
import torch
import torch.nn as nn
from torch.nn.parallel import DistributedDataParallel as DDP
from .distributed import (is_dist, is_main, get_rank, get_world_size,
all_gather_object, print_main)
from .losses import build_loss
from ..metrics.metrics import per_image_metrics
from ..data.loaders import build_dataset, build_loader
_AMP_DTYPE = {"bf16": torch.bfloat16, "fp16": torch.float16}
def build_optimizer(cfg, params):
if cfg.optimizer == "sgd":
return torch.optim.SGD(params, lr=cfg.lr, momentum=0.9,
weight_decay=cfg.weight_decay, nesterov=True)
return torch.optim.AdamW(params, lr=cfg.lr, weight_decay=cfg.weight_decay)
def lr_at(cfg, epoch: int) -> float:
if epoch < cfg.warmup_epochs:
return cfg.lr * (epoch + 1) / max(1, cfg.warmup_epochs)
e = epoch - cfg.warmup_epochs
total = max(1, cfg.epochs - cfg.warmup_epochs)
if cfg.scheduler == "poly":
return cfg.lr * (1 - e / total) ** 0.9
if cfg.scheduler == "cosine":
return cfg.lr * 0.5 * (1 + math.cos(math.pi * e / total))
return cfg.lr
class Trainer:
def __init__(self, cfg, model: nn.Module, local_rank: int):
self.cfg = cfg
self.device = torch.device("cuda", local_rank) if torch.cuda.is_available() else torch.device("cpu")
self.local_rank = local_rank
self.train_ds = build_dataset(cfg, "train")
self.val_ds = build_dataset(cfg, "val")
self.num_classes = self.train_ds.num_classes
self.train_loader = build_loader(cfg, "train", self.train_ds)
self.val_loader = build_loader(cfg, "val", self.val_ds)
self.model = model.to(self.device)
if is_dist():
self.model = DDP(self.model, device_ids=[local_rank], output_device=local_rank,
find_unused_parameters=False)
self.criterion = build_loss(cfg.loss).to(self.device)
self.optimizer = build_optimizer(cfg, self.model.parameters())
self.amp = cfg.amp
self.use_amp = self.amp in _AMP_DTYPE
self.scaler = torch.amp.GradScaler("cuda", enabled=(self.amp == "fp16"))
self.best = -1.0
self.start_epoch = 0
self.out_dir = cfg.out_dir()
if is_main():
os.makedirs(self.out_dir, exist_ok=True)
cfg.to_yaml(os.path.join(self.out_dir, "config.yaml"))
if cfg.resume:
self._load(cfg.resume)
# ---- checkpoint ----
def _bare(self):
return self.model.module if is_dist() else self.model
def _save(self, name: str, epoch: int):
if not is_main():
return
torch.save({
"epoch": epoch,
"model": self._bare().state_dict(),
"optimizer": self.optimizer.state_dict(),
"best": self.best,
"num_classes": self.num_classes,
"config": self.cfg.__dict__,
}, os.path.join(self.out_dir, name))
def _load(self, path: str):
ckpt = torch.load(path, map_location="cpu", weights_only=False)
self._bare().load_state_dict(ckpt["model"])
if "optimizer" in ckpt:
self.optimizer.load_state_dict(ckpt["optimizer"])
self.best = ckpt.get("best", -1.0)
self.start_epoch = ckpt.get("epoch", -1) + 1
print_main(f"[resume] from {path} at epoch {self.start_epoch}")
# ---- loops ----
def _autocast(self):
if self.use_amp:
return torch.autocast("cuda", dtype=_AMP_DTYPE[self.amp])
return torch.autocast("cuda", enabled=False)
def train_one_epoch(self, epoch: int):
self.model.train()
if is_dist():
self.train_loader.sampler.set_epoch(epoch)
for g in self.optimizer.param_groups:
g["lr"] = lr_at(self.cfg, epoch)
running, n = 0.0, 0
t0 = time.time()
for it, batch in enumerate(self.train_loader):
img = batch["image"].to(self.device, non_blocking=True)
msk = batch["mask"].to(self.device, non_blocking=True)
self.optimizer.zero_grad(set_to_none=True)
with self._autocast():
logits = self.model(img)
loss = self.criterion(logits, msk)
if self.amp == "fp16":
self.scaler.scale(loss).backward()
if self.cfg.grad_clip > 0:
self.scaler.unscale_(self.optimizer)
nn.utils.clip_grad_norm_(self.model.parameters(), self.cfg.grad_clip)
self.scaler.step(self.optimizer)
self.scaler.update()
else:
loss.backward()
if self.cfg.grad_clip > 0:
nn.utils.clip_grad_norm_(self.model.parameters(), self.cfg.grad_clip)
self.optimizer.step()
running += loss.item() * img.size(0)
n += img.size(0)
if is_main():
print_main(f"[ep {epoch:03d}] loss={running/max(1,n):.4f} "
f"lr={self.optimizer.param_groups[0]['lr']:.2e} "
f"({time.time()-t0:.1f}s)")
@torch.no_grad()
def validate(self) -> float:
self.model.eval()
records = []
for batch in self.val_loader:
img = batch["image"].to(self.device, non_blocking=True)
msk = batch["mask"].numpy()
with self._autocast():
logits = self.model(img)
pred = logits.argmax(1).cpu().numpy()
for i in range(pred.shape[0]):
records.append(per_image_metrics(
pred[i], msk[i], self.num_classes,
include_background=self.cfg.include_background,
compute_hd95=False))
gathered = all_gather_object(records)
flat = [r for part in gathered for r in part]
dices = np.array([r["dice"] for r in flat], dtype=np.float64)
dices = dices[~np.isnan(dices)]
return float(dices.mean()) if dices.size else 0.0
def fit(self):
best_epoch = self.start_epoch - 1
for epoch in range(self.start_epoch, self.cfg.epochs):
self.train_one_epoch(epoch)
do_val = ((epoch + 1) % self.cfg.val_interval == 0) or (epoch + 1 == self.cfg.epochs)
if do_val:
dice = self.validate()
if dice > self.best:
self.best = dice
best_epoch = epoch
self._save("best.pth", epoch)
print_main(f"[ep {epoch:03d}] val_dice={dice:.4f} "
f"(best={self.best:.4f} @ep{best_epoch})")
# early stopping: stop if val Dice hasn't improved for `patience` epochs
if (self.cfg.patience > 0 and (epoch + 1) >= self.cfg.min_epochs
and (epoch - best_epoch) >= self.cfg.patience):
print_main(f"[early-stop] no val improvement for {epoch - best_epoch} epochs "
f"(patience={self.cfg.patience}); best={self.best:.4f} @ep{best_epoch}")
self._save("last.pth", epoch)
break
if self.cfg.save_interval and (epoch + 1) % self.cfg.save_interval == 0:
self._save(f"epoch{epoch+1}.pth", epoch)
self._save("last.pth", epoch)
print_main(f"[done] best val_dice={self.best:.4f} @ep{best_epoch} -> {self.out_dir}/best.pth")
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