| """Quantify the historical strict=False teacher checkpoint key mismatch.""" |
| from pathlib import Path |
| import json |
| import sys |
|
|
| ROOT = Path(r"E:\Gaze_estimation") |
| sys.path.insert(0, str(ROOT / ".codex_deps")) |
| sys.path.insert(0, str(ROOT)) |
| import torch |
| from src.models.teacher import L2CS |
|
|
| checkpoint_path = ROOT / "checkpoints" / "resnet50.pt" |
| state = torch.load(checkpoint_path, map_location="cpu", weights_only=True) |
| model = L2CS(backbone_name="resnet50", pretrained=False) |
| result = model.load_state_dict(state, strict=False) |
| model_keys = set(model.state_dict()) |
| checkpoint_keys = set(state) |
| loaded_keys = sorted(model_keys & checkpoint_keys) |
|
|
| prefix_map = { |
| "conv1.": "backbone.0.", "bn1.": "backbone.1.", |
| "layer1.": "backbone.4.", "layer2.": "backbone.5.", |
| "layer3.": "backbone.6.", "layer4.": "backbone.7.", |
| } |
| remapped = {} |
| for key, value in state.items(): |
| new_key = key |
| for source, target in prefix_map.items(): |
| if key.startswith(source): |
| new_key = target + key[len(source):] |
| break |
| remapped[new_key] = value |
| fixed_model = L2CS(backbone_name="resnet50", pretrained=False) |
| fixed_result = fixed_model.load_state_dict(remapped, strict=False) |
|
|
| report = { |
| "checkpoint": str(checkpoint_path), |
| "checkpoint_tensor_keys": len(checkpoint_keys), |
| "wrapper_tensor_keys": len(model_keys), |
| "historical_loader_loaded_key_count": len(loaded_keys), |
| "historical_loader_loaded_keys": loaded_keys, |
| "historical_loader_missing_key_count": len(result.missing_keys), |
| "historical_loader_unexpected_key_count": len(result.unexpected_keys), |
| "historical_loader_missing_keys": result.missing_keys, |
| "historical_loader_unexpected_keys": result.unexpected_keys, |
| "remapped_loader_missing_keys": fixed_result.missing_keys, |
| "remapped_loader_unexpected_keys": fixed_result.unexpected_keys, |
| "interpretation": "Historical strict=False loading accepts only identically named keys and silently leaves the ResNet backbone at initialization/pretrained values.", |
| } |
| out = ROOT / "artifacts" / "kd-teacher-trap-diagnostic" / "teacher_loader_key_audit.json" |
| out.write_text(json.dumps(report, indent=2), encoding="utf-8") |
| print(json.dumps({k: report[k] for k in ( |
| "checkpoint_tensor_keys", "wrapper_tensor_keys", "historical_loader_loaded_key_count", |
| "historical_loader_loaded_keys", "historical_loader_missing_key_count", |
| "historical_loader_unexpected_key_count", "remapped_loader_missing_keys", |
| "remapped_loader_unexpected_keys")}, indent=2)) |
|
|
