def main()

in validate.py [0:0]

• 53 lines of code
• 6 McCabe index (conditional complexity)

def main():
    setup_default_logging()
    args = parser.parse_args()
    model_cfgs = []
    model_names = []
    if os.path.isdir(args.checkpoint):
        # validate all checkpoints in a path with same model
        checkpoints = glob.glob(args.checkpoint + '/*.pth.tar')
        checkpoints += glob.glob(args.checkpoint + '/*.pth')
        model_names = list_models(args.model)
        model_cfgs = [(args.model, c) for c in sorted(checkpoints, key=natural_key)]
    else:
        if args.model == 'all':
            # validate all models in a list of names with pretrained checkpoints
            args.pretrained = True
            model_names = list_models(
                pretrained=True,
                exclude_filters=_NON_IN1K_FILTERS,
            )
            model_cfgs = [(n, '') for n in model_names]
        elif not is_model(args.model):
            # model name doesn't exist, try as wildcard filter
            model_names = list_models(
                args.model,
                pretrained=True,
            )
            model_cfgs = [(n, '') for n in model_names]

        if not model_cfgs and os.path.isfile(args.model):
            with open(args.model) as f:
                model_names = [line.rstrip() for line in f]
            model_cfgs = [(n, None) for n in model_names if n]

    if len(model_cfgs):
        _logger.info('Running bulk validation on these pretrained models: {}'.format(', '.join(model_names)))
        results = []
        try:
            initial_batch_size = args.batch_size
            for m, c in model_cfgs:
                args.model = m
                args.checkpoint = c
                r = _try_run(args, initial_batch_size)
                if 'error' in r:
                    continue
                if args.checkpoint:
                    r['checkpoint'] = args.checkpoint
                results.append(r)
        except KeyboardInterrupt as e:
            pass
        results = sorted(results, key=lambda x: x['top1'], reverse=True)
    else:
        if args.retry:
            results = _try_run(args, args.batch_size)
        else:
            results = validate(args)

    if args.results_file:
        write_results(args.results_file, results, format=args.results_format)

    # output results in JSON to stdout w/ delimiter for runner script
    print(f'--result\n{json.dumps(results, indent=4)}')