inspect_pt.py

# /// script
# requires-python = ">=3.13"
# dependencies = ["torch", "timm"]
# ///
"""Inspect the structure of .pt checkpoint files."""

import sys

sys.path.insert(0, 'original')

import torch
from pathlib import Path


def inspect_checkpoint(path: Path) -> None:
    """Print the structure of a checkpoint file."""
    print(f'\n{"=" * 60}')
    print(f'File: {path.name} ({path.stat().st_size / 1024 / 1024:.1f} MB)')
    print('=' * 60)

    data = torch.load(path, map_location='cpu', weights_only=False)
    print(f'Top-level type: {type(data).__name__}')

    if isinstance(data, dict):
        print(f'Keys: {list(data.keys())[:10]}')
        for key, val in list(data.items())[:5]:
            if isinstance(val, torch.Tensor):
                print(f'  {key}: Tensor {val.shape} {val.dtype}')
            else:
                print(f'  {key}: {type(val).__name__}')
    elif hasattr(data, 'state_dict'):
        print('This is a full model object.')
        sd = data.state_dict()
        print(f'state_dict keys ({len(sd)}):')
        for k, v in sd.items():
            print(f'  {k}: {v.shape} {v.dtype}')
        # Print model attributes
        for attr in ['max_seq_len', 'patch_size', 'data_seq_len']:
            if hasattr(data, attr):
                print(f'  model.{attr} = {getattr(data, attr)}')
    else:
        print(f'Unexpected type: {type(data)}')


if __name__ == '__main__':
    for pt_file in sorted(Path('.').glob('*_parameters.pt')):
        inspect_checkpoint(pt_file)