EVSSM/scripts/publish_models.py

import glob
import subprocess
import torch
from os import path as osp
from torch.serialization import _is_zipfile, _open_file_like


def update_sha(paths):
    print('# Update sha ...')
    for idx, path in enumerate(paths):
        print(f'{idx+1:03d}: Processing {path}')
        net = torch.load(path, map_location=torch.device('cpu'))
        basename = osp.basename(path)
        if 'params' not in net and 'params_ema' not in net:
            user_response = input(f'WARN: Model {basename} does not have "params"/"params_ema" key. '
                                  'Do you still want to continue? Y/N\n')
            if user_response.lower() == 'y':
                pass
            elif user_response.lower() == 'n':
                raise ValueError('Please modify..')
            else:
                raise ValueError('Wrong input. Only accepts Y/N.')

        if '-' in basename:
            # check whether the sha is the latest
            old_sha = basename.split('-')[1].split('.')[0]
            new_sha = subprocess.check_output(['sha256sum', path]).decode()[:8]
            if old_sha != new_sha:
                final_file = path.split('-')[0] + f'-{new_sha}.pth'
                print(f'\tSave from {path} to {final_file}')
                subprocess.Popen(['mv', path, final_file])
        else:
            sha = subprocess.check_output(['sha256sum', path]).decode()[:8]
            final_file = path.split('.pth')[0] + f'-{sha}.pth'
            print(f'\tSave from {path} to {final_file}')
            subprocess.Popen(['mv', path, final_file])


def convert_to_backward_compatible_models(paths):
    """Convert to backward compatible pth files.

    PyTorch 1.6 uses a updated version of torch.save. In order to be compatible
    with previous PyTorch version, save it with
    _use_new_zipfile_serialization=False.
    """
    print('# Convert to backward compatible pth files ...')
    for idx, path in enumerate(paths):
        print(f'{idx+1:03d}: Processing {path}')
        flag_need_conversion = False
        with _open_file_like(path, 'rb') as opened_file:
            if _is_zipfile(opened_file):
                flag_need_conversion = True

        if flag_need_conversion:
            net = torch.load(path, map_location=torch.device('cpu'))
            print('\tConverting to compatible pth file...')
            torch.save(net, path, _use_new_zipfile_serialization=False)


if __name__ == '__main__':
    paths = glob.glob('experiments/pretrained_models/*.pth') + glob.glob('experiments/pretrained_models/**/*.pth')
    convert_to_backward_compatible_models(paths)
    update_sha(paths)