File size: 4,038 Bytes
0fad642 | 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 | """RunPod diagnostic — did TransUNet's ViT encoder actually train, or stay at ImageNet init?
Run on the pod (where the .npz, the TransUNet repo, and the trained best.pt all exist):
python check_encoder_moved.py \
--repo /workspace/TransUNet \
--npz /workspace/model/vit_checkpoint/imagenet21k/R50+ViT-B_16.npz \
--ckpt /workspace/runs/TransUNet_Phase1/repeated_holdout/stratified_holdout_v1/phase_001/pct_100/repeat_01/strategy_2/final/checkpoints/best.pt \
--img 128
Prints, per encoder tensor and overall, the relative change ||trained - imagenet_init|| / ||imagenet_init||.
If the mean change is a meaningful fraction (>~1%) and most tensors moved, the 105M encoder trained.
If it's ~0 everywhere, the encoder was effectively frozen (that would explain parity with small models).
"""
import argparse, sys, os, copy, numpy as np, torch
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--repo", required=True)
ap.add_argument("--npz", required=True)
ap.add_argument("--ckpt", required=True)
ap.add_argument("--img", type=int, default=128)
a = ap.parse_args()
sys.path.insert(0, a.repo)
from networks.vit_seg_modeling import VisionTransformer, CONFIGS
cfg = copy.deepcopy(CONFIGS["R50-ViT-B_16"])
cfg.n_classes = 1; cfg.n_skip = 3; cfg.classifier = "seg"
cfg.patches.grid = (a.img // 16, a.img // 16)
vit = VisionTransformer(cfg, img_size=a.img, num_classes=1)
# (1) ImageNet init
vit.load_from(weights=np.load(a.npz, allow_pickle=False))
init = {k: v.detach().float().clone() for k, v in vit.transformer.state_dict().items()}
# (2) trained weights from best.pt -> map "smp_model.encoder.transformer.*" -> "transformer.*"
ck = torch.load(a.ckpt, map_location="cpu", weights_only=False)
sd = ck["model_state_dict"] if "model_state_dict" in ck else ck
trained_transformer = {}
for k, v in sd.items():
if k.startswith("smp_model.encoder.transformer."):
trained_transformer[k.replace("smp_model.encoder.transformer.", "")] = v
if not trained_transformer:
print("!! No 'smp_model.encoder.transformer.*' keys in checkpoint. Keys sample:")
print([k for k in list(sd)[:10]]); return
missing, unexpected = vit.transformer.load_state_dict(trained_transformer, strict=False)
print(f"loaded trained transformer tensors={len(trained_transformer)} (missing={len(missing)} unexpected={len(unexpected)})")
trained = {k: v.detach().float().clone() for k, v in vit.transformer.state_dict().items()}
# (3) compare
rows = []
for k in init:
if init[k].numel() == 0 or "num_batches_tracked" in k:
continue
num = (trained[k] - init[k]).norm().item()
den = init[k].norm().item() + 1e-12
rows.append((k, num / den, init[k].numel()))
rel = np.array([r[1] for r in rows]); npar = np.array([r[2] for r in rows])
wmean = float((rel * npar).sum() / npar.sum()) # param-weighted mean rel change
print("\n=== ENCODER MOVEMENT (relative change from ImageNet init) ===")
print(f"tensors compared : {len(rows)}")
print(f"param-weighted mean : {wmean*100:.3f}%")
print(f"median tensor change : {np.median(rel)*100:.3f}%")
print(f"max tensor change : {rel.max()*100:.3f}%")
print(f"tensors moved >1% : {int((rel>0.01).sum())}/{len(rows)}")
print(f"tensors basically 0 : {int((rel<1e-4).sum())}/{len(rows)} (<0.01% change)")
print("\nexamples:")
for name in ["embeddings.position_embeddings",
"embeddings.hybrid_model.root.conv.weight",
"encoder.layer.0.attn.query.weight",
"encoder.layer.11.ffn.fc1.weight"]:
m = {r[0]: r[1] for r in rows}.get(name)
if m is not None: print(f" {name:52s} {m*100:7.3f}%")
print("\nVERDICT:", "ENCODER TRAINED (capacity used)" if wmean > 0.005
else "ENCODER BARELY MOVED (effectively frozen -> tuning target)")
if __name__ == "__main__":
main()
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