code: 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=["*_in21k", "*_in22k"],
              )
              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)
              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):
          results_file = args.results_file or "./results-all.csv"
          _logger.info(
              "Running bulk validation on these pretrained models: {}".format(
                  ", ".join(model_names)
              )
          )
          results = []
          try:
              start_batch_size = args.batch_size
              for m, c in model_cfgs:
                  batch_size = start_batch_size
                  args.model = m
                  args.checkpoint = c
                  result = OrderedDict(model=args.model)
                  r = {}
                  while not r and batch_size >= args.num_gpu:
                      torch.cuda.empty_cache()
                      try:
                          args.batch_size = batch_size
                          print(
                              "Validating with batch size: %d"
                              % args.batch_size
                          )
                          r = validate(args)
                      except RuntimeError as e:
                          if batch_size <= args.num_gpu:
                              print(
                                  "Validation failed with no ability to reduce batch size. Exiting."
                              )
                              raise e
                          batch_size = max(
                              batch_size // 2, args.num_gpu
                          )
                          print(
                              "Validation failed, reducing batch size by 50%"
                          )
                  result.update(r)
                  if args.checkpoint:
                      result["checkpoint"] = args.checkpoint
                  results.append(result)
          except KeyboardInterrupt as e:
              pass
          results = sorted(
              results, key=lambda x: x["top1"], reverse=True
          )
          if len(results):
              write_results(results_file, results)
      else:
          validate(args)
  
file_path: ml-fastvit/validate.py
imports: import argparse
  import os
  import csv
  import glob
  import time
  import logging
  import torch
  import torch.nn
  import torch.nn.parallel
  from collections import OrderedDict
  from contextlib import suppress
  from timm.layers import apply_test_time_pool
  from timm.models import create_model, load_checkpoint, is_model, list_models
  from timm.data import create_dataset, create_loader, resolve_data_config, RealLabelsImagenet
  from timm.utils import accuracy, AverageMeter, natural_key, setup_default_logging, set_jit_legacy
  import models
  from models.modules.mobileone import reparameterize_model
label_desc: Use image node_id 2 for calling a global function with example usage: main() without return types