Datasets:
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# -*- coding: utf-8 -*-
"""
Train ResNet / ViT baselines (timm, ImageNet-pretrained) on one dataset.
Same data layout, augmentation, optimizer and model-selection rule as the RETFound
runs so the three models are directly comparable. After training it reloads the
best-val checkpoint, predicts the test set and saves:
<output_dir>/<task>/checkpoint-best.pth
<output_dir>/<task>/test_pred.npz (y_true, y_prob) <- consumed by evaluate.py
<output_dir>/<task>/log.csv
Model selection score = (f1_macro + auroc + kappa)/3 on val (mirrors RETFound).
"""
import os, csv, json, math, argparse
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
import timm
from timm.data import resolve_data_config, create_transform
from torchvision import datasets, transforms
from sklearn.metrics import f1_score, roc_auc_score, cohen_kappa_score, accuracy_score
IMAGENET_MEAN = (0.485, 0.456, 0.406)
IMAGENET_STD = (0.229, 0.224, 0.225)
def build_loaders(data_path, input_size, batch_size, workers):
train_tf = transforms.Compose([
transforms.RandomResizedCrop(input_size, scale=(0.6, 1.0)),
transforms.RandomHorizontalFlip(),
transforms.ColorJitter(0.1, 0.1, 0.1),
transforms.ToTensor(),
transforms.Normalize(IMAGENET_MEAN, IMAGENET_STD),
])
eval_tf = transforms.Compose([
transforms.Resize(int(input_size * 1.15)),
transforms.CenterCrop(input_size),
transforms.ToTensor(),
transforms.Normalize(IMAGENET_MEAN, IMAGENET_STD),
])
ds = {s: datasets.ImageFolder(os.path.join(data_path, s),
train_tf if s == "train" else eval_tf)
for s in ["train", "val", "test"]}
ld = {s: DataLoader(ds[s], batch_size=batch_size, shuffle=(s == "train"),
num_workers=workers, pin_memory=True, drop_last=(s == "train"))
for s in ds}
return ds, ld
@torch.no_grad()
def predict(model, loader, device):
model.eval()
probs, labels = [], []
for x, y in loader:
x = x.to(device, non_blocking=True)
with torch.cuda.amp.autocast():
out = model(x)
probs.append(F.softmax(out.float(), 1).cpu().numpy())
labels.append(y.numpy())
return np.concatenate(labels), np.concatenate(probs)
def val_score(y_true, y_prob):
C = y_prob.shape[1]
y_pred = y_prob.argmax(1)
f1 = f1_score(y_true, y_pred, average="macro", zero_division=0)
kap = cohen_kappa_score(y_true, y_pred)
try:
if C == 2:
auc = roc_auc_score(y_true, y_prob[:, 1])
else:
auc = roc_auc_score(np.eye(C)[y_true], y_prob, multi_class="ovr", average="macro")
except Exception:
auc = 0.0
return (f1 + auc + kap) / 3, accuracy_score(y_true, y_pred), auc
def build_param_groups(model, weight_decay, layer_decay):
"""Split params into (weight-decay / no-decay) groups.
If layer_decay < 1 and the model is a ViT (has .blocks), apply layer-wise lr
decay (MAE/BeiT style): shallower layers get exponentially smaller lr via lr_scale.
no-decay = biases, 1-D params (norms), cls_token, pos_embed."""
blocks = getattr(model, "blocks", None)
use_lld = (blocks is not None) and (layer_decay < 1.0)
num_layers = (len(blocks) + 1) if blocks is not None else 1
def layer_id(name):
if name in ("cls_token", "pos_embed") or name.startswith("patch_embed"):
return 0
if name.startswith("blocks."):
return int(name.split(".")[1]) + 1
return num_layers
groups = {}
for n, p in model.named_parameters():
if not p.requires_grad:
continue
no_decay = (p.ndim == 1 or n.endswith(".bias") or n in ("cls_token", "pos_embed"))
lid = layer_id(n) if use_lld else 0
scale = (layer_decay ** (num_layers - lid)) if use_lld else 1.0
key = (lid, no_decay)
if key not in groups:
groups[key] = {"params": [], "weight_decay": 0.0 if no_decay else weight_decay,
"lr_scale": scale}
groups[key]["params"].append(p)
return list(groups.values())
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--data_path", required=True)
ap.add_argument("--nb_classes", type=int, required=True)
ap.add_argument("--model", required=True, help="timm name e.g. resnet50 / vit_base_patch16_224")
ap.add_argument("--input_size", type=int, default=224)
ap.add_argument("--batch_size", type=int, default=64)
ap.add_argument("--epochs", type=int, default=50)
ap.add_argument("--lr", type=float, default=5e-4)
ap.add_argument("--weight_decay", type=float, default=0.05)
ap.add_argument("--warmup_epochs", type=int, default=3)
ap.add_argument("--patience", type=int, default=10)
# ViT-friendly fine-tuning knobs (defaults OFF -> identical to old behaviour)
ap.add_argument("--layer_decay", type=float, default=1.0, help="<1.0 enables layer-wise lr decay (ViT)")
ap.add_argument("--drop_path", type=float, default=0.0, help="stochastic depth rate (ViT)")
ap.add_argument("--label_smoothing", type=float, default=0.0)
ap.add_argument("--workers", type=int, default=8)
ap.add_argument("--output_dir", required=True)
ap.add_argument("--task", required=True)
args = ap.parse_args()
out = os.path.join(args.output_dir, args.task)
os.makedirs(out, exist_ok=True)
device = "cuda" if torch.cuda.is_available() else "cpu"
torch.manual_seed(42); np.random.seed(42)
ds, ld = build_loaders(args.data_path, args.input_size, args.batch_size, args.workers)
print(f"[{args.task}] train={len(ds['train'])} val={len(ds['val'])} test={len(ds['test'])} "
f"classes={ds['train'].classes}")
model = timm.create_model(args.model, pretrained=True, num_classes=args.nb_classes,
drop_path_rate=args.drop_path).to(device)
# class-weighted loss (+ optional label smoothing) from train distribution
counts = np.bincount([y for _, y in ds["train"].samples], minlength=args.nb_classes)
w = counts.sum() / (args.nb_classes * np.clip(counts, 1, None))
criterion = nn.CrossEntropyLoss(weight=torch.tensor(w, dtype=torch.float32, device=device),
label_smoothing=args.label_smoothing)
groups = build_param_groups(model, args.weight_decay, args.layer_decay)
opt = torch.optim.AdamW(groups, lr=args.lr, weight_decay=args.weight_decay)
print(f"[{args.task}] optim groups={len(groups)} layer_decay={args.layer_decay} "
f"drop_path={args.drop_path} ls={args.label_smoothing} lr={args.lr}")
scaler = torch.cuda.amp.GradScaler()
steps = len(ld["train"])
def lr_at(ep_frac): # warmup + cosine
if ep_frac < args.warmup_epochs:
return args.lr * ep_frac / max(1, args.warmup_epochs)
p = (ep_frac - args.warmup_epochs) / max(1, args.epochs - args.warmup_epochs)
return args.lr * 0.5 * (1 + math.cos(math.pi * min(1.0, p)))
log_path = os.path.join(out, "log.csv")
lf = open(log_path, "w", newline=""); lw = csv.writer(lf)
lw.writerow(["epoch", "train_loss", "val_acc", "val_auc", "val_score", "lr"]); lf.flush()
best_score, best_ep, since = -1, -1, 0
ckpt = os.path.join(out, "checkpoint-best.pth")
for ep in range(args.epochs):
model.train(); running = 0.0
for it, (x, y) in enumerate(ld["train"]):
for g in opt.param_groups:
g["lr"] = lr_at(ep + it / steps) * g.get("lr_scale", 1.0)
x, y = x.to(device, non_blocking=True), y.to(device, non_blocking=True)
opt.zero_grad()
with torch.cuda.amp.autocast():
loss = criterion(model(x), y)
scaler.scale(loss).backward(); scaler.step(opt); scaler.update()
running += loss.item()
yv, pv = predict(model, ld["val"], device)
sc, vacc, vauc = val_score(yv, pv)
lw.writerow([ep, running / steps, vacc, vauc, sc, opt.param_groups[0]["lr"]]); lf.flush()
print(f"[{args.task}] ep{ep} loss={running/steps:.4f} val_acc={vacc:.4f} "
f"val_auc={vauc:.4f} score={sc:.4f}")
if sc > best_score:
best_score, best_ep, since = sc, ep, 0
torch.save({"model": model.state_dict(), "epoch": ep, "val_score": sc}, ckpt)
else:
since += 1
if since >= args.patience:
print(f"[{args.task}] early stop at ep{ep} (best ep{best_ep} score={best_score:.4f})")
break
lf.close()
# reload best, predict test, save raw predictions for unified evaluate.py
state = torch.load(ckpt, map_location="cpu", weights_only=False)
model.load_state_dict(state["model"]); model.to(device)
yt, pt = predict(model, ld["test"], device)
np.savez(os.path.join(out, "test_pred.npz"), y_true=yt, y_prob=pt)
print(f"[{args.task}] DONE best_ep={best_ep} best_val_score={best_score:.4f} "
f"-> saved test_pred.npz ({len(yt)} samples)")
if __name__ == "__main__":
main()
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