def main()

in bulk_runner.py [0:0]

• 78 lines of code
• 7 McCabe index (conditional complexity)

def main():
    args = parser.parse_args()
    cmd, cmd_args = cmd_from_args(args)

    model_cfgs = []
    if args.model_list == 'all':
        model_names = list_models(
            pretrained=args.pretrained,  # only include models w/ pretrained checkpoints if set
        )
        model_cfgs = [(n, None) for n in model_names]
    elif args.model_list == 'all_in1k':
        model_names = list_models(pretrained=True)
        model_cfgs = _get_model_cfgs(model_names, num_classes=1000, expand_train_test=True)
    elif args.model_list == 'all_res':
        model_names = list_models()
        model_cfgs = _get_model_cfgs(model_names, expand_train_test=True, include_crop=False, expand_arch=True)
    elif not is_model(args.model_list):
        # model name doesn't exist, try as wildcard filter
        model_names = list_models(args.model_list)
        model_cfgs = [(n, None) for n in model_names]

    if not model_cfgs and os.path.exists(args.model_list):
        with open(args.model_list) as f:
            model_names = [line.rstrip() for line in f]
            model_cfgs = _get_model_cfgs(
                model_names,
                #num_classes=1000,
                expand_train_test=True,
                #include_crop=False,
            )

    if len(model_cfgs):
        results_file = args.results_file or './results.csv'
        results = []
        errors = []
        model_strings = '\n'.join([f'{x[0]}, {x[1]}' for x in model_cfgs])
        print(f"Running script on these models:\n {model_strings}")
        if not args.sort_key:
            if 'benchmark' in args.script:
                if any(['train' in a for a in args.script_args]):
                    sort_key = 'train_samples_per_sec'
                else:
                    sort_key = 'infer_samples_per_sec'
            else:
                sort_key = 'top1'
        else:
            sort_key = args.sort_key
        print(f'Script: {args.script}, Args: {args.script_args}, Sort key: {sort_key}')

        try:
            for m, ax in model_cfgs:
                if not m:
                    continue
                args_str = (cmd, *[str(e) for e in cmd_args], '--model', m)
                if ax is not None:
                    extra_args = [(f'--{k}', str(v)) for k, v in ax.items()]
                    extra_args = [i for t in extra_args for i in t]
                    args_str += tuple(extra_args)
                try:
                    o = subprocess.check_output(args=args_str).decode('utf-8').split('--result')[-1]
                    r = json.loads(o)
                    results.append(r)
                except Exception as e:
                    # FIXME batch_size retry loop is currently done in either validation.py or benchmark.py
                    # for further robustness (but more overhead), we may want to manage that by looping here...
                    errors.append(dict(model=m, error=str(e)))
                if args.delay:
                    time.sleep(args.delay)
        except KeyboardInterrupt as e:
            pass

        errors.extend(list(filter(lambda x: 'error' in x, results)))
        if errors:
            print(f'{len(errors)} models had errors during run.')
            for e in errors:
                if 'model' in e:
                    print(f"\t {e['model']} ({e.get('error', 'Unknown')})")
                else:
                    print(e)

        results = list(filter(lambda x: 'error' not in x, results))

        no_sortkey = list(filter(lambda x: sort_key not in x, results))
        if no_sortkey:
            print(f'{len(no_sortkey)} results missing sort key, skipping sort.')
        else:
            results = sorted(results, key=lambda x: x[sort_key], reverse=True)

        if len(results):
            print(f'{len(results)} models run successfully. Saving results to {results_file}.')
            write_results(results_file, results)