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f211247 | 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 | #!/usr/bin/env python3
"""Multi-task: classification + bbox-normalized position (reading order hint).
Auxiliary head predicts (x_center, y_center) normalized to tablet bbox.
Forces backbone to encode spatial context → better discrimination for
signs that only differ in tablet position (numerals, logograms).
"""
import os, sys, json, argparse, time
from pathlib import Path
from collections import Counter
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader, WeightedRandomSampler
from PIL import Image
ROOT = Path("/arf/scratch/stakan/hitit-proje")
sys.path.insert(0, str(ROOT / "hitit_ocr/src"))
from train_classification import build_backbone, get_arch_img_size
def log(m): print(f"[{time.strftime('%H:%M:%S')}] {m}", flush=True)
class PosDS(Dataset):
def __init__(self, manifest, label_to_idx, img_size, is_train, val_fold, min_samples):
cls_count = Counter()
self.records = []
for line in open(manifest):
r = json.loads(line)
if r.get('task') != 'classification' or not r.get('unified_label'): continue
cls_count[r['unified_label']] += 1
for line in open(manifest):
r = json.loads(line)
if r.get('task') != 'classification': continue
if not r.get('unified_label') or r['unified_label'] not in label_to_idx: continue
if not r.get('path') or r.get('storage') != 'fs': continue
if r.get('integrity_ok') is False: continue
if cls_count[r['unified_label']] < min_samples: continue
fold = r.get('tablet_view_fold', 0)
if is_train and fold == val_fold: continue
if not is_train and fold != val_fold: continue
self.records.append(r)
self.l2i = label_to_idx
from torchvision import transforms
self.tf = transforms.Compose([
transforms.Resize((img_size, img_size), antialias=True),
transforms.ToTensor(),
transforms.Normalize([0.489, 0.448, 0.424], [0.362, 0.359, 0.364]),
])
def __len__(self): return len(self.records)
def __getitem__(self, i):
r = self.records[i]
img = Image.open(r['path']).convert('RGB')
# position from bbox if available, else 0.5,0.5
bbox = r.get('bbox_normalized') or r.get('bbox') or [0.25, 0.25, 0.75, 0.75]
if isinstance(bbox, list) and len(bbox) == 4:
xc = 0.5 * (bbox[0] + bbox[2]); yc = 0.5 * (bbox[1] + bbox[3])
else:
xc, yc = 0.5, 0.5
pos = torch.tensor([xc, yc], dtype=torch.float32)
return self.tf(img), self.l2i[r['unified_label']], pos
class MultiTaskHead(nn.Module):
def __init__(self, feat_dim, n_classes):
super().__init__()
self.cls = nn.Linear(feat_dim, n_classes)
self.pos = nn.Sequential(
nn.Linear(feat_dim, 128), nn.GELU(), nn.Linear(128, 2), nn.Sigmoid()
)
def forward(self, feats):
return self.cls(feats), self.pos(feats)
@torch.no_grad()
def extract(model, x, dtype):
raw = model.module if hasattr(model, 'module') else model
with torch.amp.autocast('cuda', dtype=dtype, enabled=True):
if hasattr(raw, 'forward_features'):
f = raw.forward_features(x)
if f.dim() == 3: f = f[:, 0]
elif f.dim() == 4: f = f.mean(dim=(2, 3))
else:
f = raw(x)
return f.float()
def main():
ap = argparse.ArgumentParser()
ap.add_argument('--ckpt', required=True)
ap.add_argument('--manifest', required=True)
ap.add_argument('--val-fold', type=int, default=0)
ap.add_argument('--min-samples', type=int, default=10)
ap.add_argument('--epochs', type=int, default=30)
ap.add_argument('--lr', type=float, default=5e-3)
ap.add_argument('--pos-weight', type=float, default=0.3)
ap.add_argument('--batch-size', type=int, default=128)
ap.add_argument('--output', required=True)
args = ap.parse_args()
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
dtype = torch.bfloat16 if torch.cuda.is_available() and torch.cuda.is_bf16_supported() else torch.float32
arch, path = args.ckpt.split(':', 1)
ck = torch.load(path, map_location='cpu', weights_only=False)
label_to_idx = ck['label_to_idx']
n_cls = len(label_to_idx)
img_size = get_arch_img_size(arch)
bb = build_backbone(arch, n_classes=n_cls, img_size_override=img_size).to(device)
sd = ck['model']
sd = {k.replace('module.', '', 1): v for k, v in sd.items()}
sd = {k.replace('_orig_mod.', '', 1): v for k, v in sd.items()}
sd = {k: v for k, v in sd.items() if k != 'n_averaged'}
bb.load_state_dict(sd, strict=False)
for p in bb.parameters(): p.requires_grad = False
bb.eval()
tr_ds = PosDS(args.manifest, label_to_idx, img_size, True, args.val_fold, args.min_samples)
va_ds = PosDS(args.manifest, label_to_idx, img_size, False, args.val_fold, args.min_samples)
log(f"Train={len(tr_ds)} Val={len(va_ds)}")
cc = Counter(tr_ds.l2i[r['unified_label']] for r in tr_ds.records)
sample_w = np.array([1.0 / max(1, cc[tr_ds.l2i[r['unified_label']]])**0.5
for r in tr_ds.records], dtype=np.float32)
tr_dl = DataLoader(tr_ds, batch_size=args.batch_size,
sampler=WeightedRandomSampler(sample_w, len(tr_ds), True),
num_workers=6, pin_memory=True, drop_last=True)
va_dl = DataLoader(va_ds, batch_size=args.batch_size, shuffle=False,
num_workers=4, pin_memory=True)
with torch.no_grad():
feat_dim = extract(bb, torch.zeros(1, 3, img_size, img_size, device=device), dtype).size(-1)
head = MultiTaskHead(feat_dim, n_cls).to(device)
opt = torch.optim.AdamW(head.parameters(), lr=args.lr, weight_decay=1e-4)
sched = torch.optim.lr_scheduler.CosineAnnealingLR(opt, T_max=args.epochs)
best, best_probs, best_y = 0, None, None
for ep in range(args.epochs):
head.train()
tl, nb = 0, 0
for x, y, pos in tr_dl:
x = x.to(device, non_blocking=True); y = y.to(device, non_blocking=True)
pos = pos.to(device, non_blocking=True)
f = extract(bb, x, dtype)
logits_cls, pred_pos = head(f)
loss_cls = F.cross_entropy(logits_cls, y, label_smoothing=0.1)
loss_pos = F.mse_loss(pred_pos, pos)
loss = loss_cls + args.pos_weight * loss_pos
opt.zero_grad(); loss.backward(); opt.step()
tl += loss.item(); nb += 1
sched.step()
head.eval()
cs, tot, p_all, y_all = 0, 0, [], []
with torch.no_grad():
for x, y, _ in va_dl:
x = x.to(device, non_blocking=True); y = y.to(device, non_blocking=True)
f = extract(bb, x, dtype)
logits, _ = head(f)
p = F.softmax(logits.float(), dim=-1)
p_all.append(p.cpu()); y_all.append(y.cpu())
cs += (logits.argmax(-1) == y).sum().item(); tot += y.size(0)
acc = cs / max(1, tot)
if ep % 5 == 0 or ep == args.epochs - 1:
log(f"ep {ep}: loss={tl/max(1,nb):.4f} val_top1={acc:.4f}")
if acc > best:
best = acc
best_probs = torch.cat(p_all); best_y = torch.cat(y_all)
torch.save({'head_state': head.state_dict(), 'label_to_idx': label_to_idx,
'probs': best_probs, 'targets': best_y, 'top1': best,
'backbone_arch': arch},
args.output)
log(f"=== MultiTask BEST: {best:.4f}")
if __name__ == '__main__':
main()
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