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c75b31a | 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 | """Stage 4B training: cosine loss on full 768-D pooled teacher output, 30 epochs."""
import os, sys, time, json, math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from PIL import Image
from pycocotools.coco import COCO
from safetensors.torch import save_file
HERE = os.path.dirname(os.path.abspath(__file__))
sys.path.insert(0, HERE)
from student import Stage4BStudent
COCO_ROOT = '/home/zootest/datasets/coco'
TARGETS = f'{COCO_ROOT}/stage4b_teacher_targets/targets.pt'
CLASSIFIER = '/mnt/d/_tmp/1pc_repo/stage_0/classifier.json'
OUT_DIR = '/mnt/d/_tmp/1pc_repo/stage_4b'
DEVICE = 'cuda'
RES = 768
BATCH = 16
LR = 5e-4
WD = 1e-4
EPOCHS = 15
WARMUP_FRAC = 0.03
class CocoImgDataset(torch.utils.data.Dataset):
def __init__(self, coco_root, pack):
self.root = f'{coco_root}/train2017'
coco = COCO(f'{coco_root}/annotations/instances_train2017.json')
self.img_ids = pack['img_ids']
self.targets = pack['targets']
self.id_to_file = {i['id']: i['file_name']
for i in coco.loadImgs(coco.getImgIds())}
def __len__(self):
return len(self.img_ids)
def __getitem__(self, i):
img_id = self.img_ids[i]
target = self.targets[i].float()
fname = self.id_to_file.get(img_id)
if fname is None:
return None
try:
img = Image.open(f'{self.root}/{fname}').convert('RGB').resize((RES, RES), Image.BILINEAR)
except Exception:
return None
arr = np.asarray(img, dtype=np.uint8).copy()
x = torch.from_numpy(arr).permute(2, 0, 1).float() / 255.0
mean = torch.tensor([0.485, 0.456, 0.406]).view(3, 1, 1)
std = torch.tensor([0.229, 0.224, 0.225]).view(3, 1, 1)
return (x - mean) / std, target
def collate(batch):
batch = [b for b in batch if b is not None]
if not batch:
return None
xs, ts = zip(*batch)
return torch.stack(xs), torch.stack(ts)
def cosine_loss(pred, target):
# 1 - cosine similarity (per-sample mean)
return (1.0 - F.cosine_similarity(pred, target, dim=-1)).mean()
def eval_f1_via_stage0(student, classifier_json, n=500):
with open(classifier_json) as f:
c = json.load(f)
pos = torch.tensor(c['pos_dims'], device=DEVICE)
neg = torch.tensor(c['neg_dims'], device=DEVICE)
thr = c['threshold']
coco = COCO(f'{COCO_ROOT}/annotations/instances_val2017.json')
img_ids = sorted(coco.getImgIds())[:n]
id_to_file = {i['id']: i['file_name']
for i in coco.loadImgs(coco.getImgIds())}
MEAN = torch.tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1).to(DEVICE)
STD = torch.tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1).to(DEVICE)
scores, labels = [], []
student.eval()
with torch.inference_mode():
for img_id in img_ids:
fname = id_to_file.get(img_id)
if not fname:
continue
img = Image.open(f'{COCO_ROOT}/val2017/{fname}').convert('RGB').resize((RES, RES), Image.BILINEAR)
arr = np.asarray(img, dtype=np.uint8).copy()
x = torch.from_numpy(arr).permute(2, 0, 1).unsqueeze(0).to(DEVICE).float() / 255.0
x = (x - MEAN) / STD
with torch.autocast('cuda', dtype=torch.bfloat16):
out = student(x).float() # (1, 768)
s = (out[0, pos].sum() - out[0, neg].sum()).item()
scores.append(s)
labels.append(any(a['category_id'] == 1
for a in coco.loadAnns(coco.getAnnIds(imgIds=img_id, iscrowd=False))))
scores = torch.tensor(scores)
labels = torch.tensor(labels, dtype=torch.bool)
uniq = torch.unique(scores).sort().values
best = (0, 0, 0, 0)
for t in uniq.tolist()[::max(1, len(uniq) // 500)]:
pred = scores > t
tp = (pred & labels).sum().float()
fp = (pred & ~labels).sum().float()
fn = (~pred & labels).sum().float()
prec = tp / (tp + fp).clamp(min=1)
rec = tp / (tp + fn).clamp(min=1)
f1 = (2 * prec * rec / (prec + rec).clamp(min=1e-9)).item()
if f1 > best[0]:
best = (f1, t, prec.item(), rec.item())
return best
def main():
os.makedirs(OUT_DIR, exist_ok=True)
print('[init] loading targets', flush=True)
pack = torch.load(TARGETS, map_location='cpu', weights_only=False)
print(f' {pack["targets"].shape[0]} teacher targets, dim {pack["targets"].shape[1]}', flush=True)
ds = CocoImgDataset(COCO_ROOT, pack)
loader = torch.utils.data.DataLoader(
ds, batch_size=BATCH, shuffle=True, num_workers=4,
pin_memory=True, collate_fn=collate, drop_last=True)
student = Stage4BStudent().to(DEVICE)
nparams = sum(p.numel() for p in student.parameters())
print(f'[student] {nparams:,} params = {nparams/1e6:.2f}M', flush=True)
total_steps = EPOCHS * len(loader)
warmup = int(total_steps * WARMUP_FRAC)
opt = torch.optim.AdamW(student.parameters(), lr=LR, weight_decay=WD)
sched = torch.optim.lr_scheduler.LambdaLR(
opt, lambda s: s / max(1, warmup) if s < warmup
else 0.5 * (1 + math.cos(math.pi * (s - warmup) / max(1, total_steps - warmup))))
log = {'student_params': nparams, 'loss': 'cosine_1_minus_sim', 'target_dim': 768, 'epochs': []}
step = 0; t0 = time.time()
for ep in range(EPOCHS):
student.train()
ep_loss, n_batches = 0.0, 0
for batch in loader:
if batch is None:
continue
x, y = batch
x = x.to(DEVICE, non_blocking=True); y = y.to(DEVICE, non_blocking=True)
with torch.autocast('cuda', dtype=torch.bfloat16):
pred = student(x)
loss = cosine_loss(pred.float(), y)
opt.zero_grad(set_to_none=True)
loss.backward()
torch.nn.utils.clip_grad_norm_(student.parameters(), 1.0)
opt.step(); sched.step()
ep_loss += loss.item(); n_batches += 1; step += 1
if step % 500 == 0:
print(f' ep {ep+1}/{EPOCHS} step {step}/{total_steps} '
f'loss={loss.item():.4f} lr={opt.param_groups[0]["lr"]:.2e} '
f'{(time.time()-t0)/60:.1f} min', flush=True)
avg = ep_loss / max(1, n_batches)
f1, thr, p, r = eval_f1_via_stage0(student, CLASSIFIER)
print(f'[ep {ep+1}] loss={avg:.4f} F1={f1:.4f} P={p:.4f} R={r:.4f} '
f'θ={thr:.3f} {(time.time()-t0)/60:.1f} min', flush=True)
log['epochs'].append({'epoch': ep + 1, 'loss': avg,
'F1': f1, 'precision': p, 'recall': r, 'threshold': thr})
if (ep + 1) % 5 == 0 or ep == EPOCHS - 1:
save_file(student.state_dict(), f'{OUT_DIR}/student_ep{ep+1}.safetensors')
with open(f'{OUT_DIR}/training_log.json', 'w') as f:
json.dump(log, f, indent=2)
save_file(student.state_dict(), f'{OUT_DIR}/student_final.safetensors')
print(f'[done] total {(time.time()-t0)/60:.1f} min', flush=True)
if __name__ == '__main__':
main()
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