stage_4/prepare_targets.py

"""Build the per-image 100-D teacher target tensor from the existing ViT-B
COCO train feature cache. One-time, ~5 min.

For each image in /home/zootest/datasets/coco/feature_cache_768 (the 419 GB
train cache of EUPE-ViT-B at 768 px), apply LayerNorm across the 768 channel
axis, max-pool across 2304 patches, and select the 100 classifier-relevant
dims (pos + neg). Save as a flat (N, 100) float16 tensor + index list.

Output: /home/zootest/datasets/coco/stage4_teacher_targets/targets.pt
        containing {'targets': (N, 100) float16, 'img_ids': list, 'dims': (100,) long}
"""
import os, glob, json, time
import torch
import torch.nn.functional as F

COCO_ROOT = '/home/zootest/datasets/coco'
CACHE = f'{COCO_ROOT}/feature_cache_768'
CLASSIFIER = '/mnt/d/_tmp/1pc_repo/stage_0/classifier.json'
OUT_DIR = f'{COCO_ROOT}/stage4_teacher_targets'
D = 768


def main():
    os.makedirs(OUT_DIR, exist_ok=True)
    with open(CLASSIFIER) as f:
        c = json.load(f)
    dims = torch.tensor(c['pos_dims'] + c['neg_dims'], dtype=torch.long)
    print(f'[init] using {len(dims)} target dims', flush=True)

    shards = sorted(glob.glob(f'{CACHE}/shard_*.pt'))
    print(f'[init] {len(shards)} teacher shards to process', flush=True)

    all_targets = []
    all_img_ids = []
    t0 = time.time()
    for si, spath in enumerate(shards):
        shard = torch.load(spath, map_location='cpu', weights_only=False)
        for entry in shard:
            sp = entry['spatial'].float()                            # (768, 48, 48)
            ln = F.layer_norm(sp.permute(1, 2, 0).reshape(-1, D), [D])
            pooled = ln.max(dim=0).values                             # (768,)
            target = pooled[dims].half()                              # (100,)
            all_targets.append(target)
            # Some caches may not have img_id; use an ordinal if missing
            img_id = entry.get('img_id', len(all_img_ids))
            all_img_ids.append(int(img_id) if isinstance(img_id, (int, float)) else len(all_img_ids))
        elapsed = time.time() - t0
        rate = (si + 1) / max(elapsed, 1e-6)
        eta = (len(shards) - si - 1) / max(rate, 1e-6)
        print(f'  shard {si+1}/{len(shards)}  cumulative n={len(all_targets)}  '
              f'{elapsed:.0f}s  ETA {eta:.1f}s', flush=True)

    targets = torch.stack(all_targets)
    torch.save({
        'targets': targets,
        'img_ids': all_img_ids,
        'dims': dims,
    }, f'{OUT_DIR}/targets.pt')
    print(f'[done] {targets.shape[0]} targets shape {tuple(targets.shape)} '
          f'-> {OUT_DIR}/targets.pt', flush=True)


if __name__ == '__main__':
    main()