code: def validate(args):
      # might as well try to validate something
      args.pretrained = args.pretrained or not args.checkpoint
      args.prefetcher = not args.no_prefetcher
      amp_autocast = suppress  # do nothing
      if args.amp:
          if has_native_amp:
              args.native_amp = True
          elif has_apex:
              args.apex_amp = True
          else:
              _logger.warning(
                  "Neither APEX or Native Torch AMP is available."
              )
      assert (
          not args.apex_amp or not args.native_amp
      ), "Only one AMP mode should be set."
      if args.native_amp:
          amp_autocast = torch.cuda.amp.autocast
          _logger.info(
              "Validating in mixed precision with native PyTorch AMP."
          )
      elif args.apex_amp:
          _logger.info(
              "Validating in mixed precision with NVIDIA APEX AMP."
          )
      else:
          _logger.info("Validating in float32. AMP not enabled.")
  
      if args.legacy_jit:
          set_jit_legacy()
  
      # create model
      model = create_model(
          args.model,
          pretrained=args.pretrained,
          num_classes=args.num_classes,
          in_chans=3,
          global_pool=args.gp,
          scriptable=args.torchscript,
          inference_mode=args.use_inference_mode,
      )
      if args.num_classes is None:
          assert hasattr(
              model, "num_classes"
          ), "Model must have `num_classes` attr if not set on cmd line/config."
          args.num_classes = model.num_classes
  
      if args.checkpoint:
          load_checkpoint(model, args.checkpoint, args.use_ema)
  
      # Reparameterize model
      model.eval()
      if not args.use_inference_mode:
          _logger.info("Reparameterizing Model %s" % (args.model))
          model = reparameterize_model(model)
      setattr(model, "pretrained_cfg", model.__dict__["default_cfg"])
  
      data_config = resolve_data_config(
          vars(args), model=model, use_test_size=True, verbose=True
      )
      test_time_pool = False
      if args.test_pool:
          model, test_time_pool = apply_test_time_pool(
              model, data_config, use_test_size=True
          )
  
      if args.torchscript:
          torch.jit.optimized_execution(True)
          model = torch.jit.script(model)
  
      model = model.cuda()
      if args.apex_amp:
          model = amp.initialize(model, opt_level="O1")
  
      if args.channels_last:
          model = model.to(memory_format=torch.channels_last)
  
      if args.num_gpu > 1:
          model = torch.nn.DataParallel(
              model, device_ids=list(range(args.num_gpu))
          )
  
      criterion = nn.CrossEntropyLoss().cuda()
  
      dataset = create_dataset(
          root=args.data,
          name=args.dataset,
          split=args.split,
          download=args.dataset_download,
          load_bytes=args.tf_preprocessing,
          class_map=args.class_map,
      )
  
      if args.valid_labels:
          with open(args.valid_labels, "r") as f:
              valid_labels = {int(line.rstrip()) for line in f}
              valid_labels = [
                  i in valid_labels for i in range(args.num_classes)
              ]
      else:
          valid_labels = None
  
      if args.real_labels:
          real_labels = RealLabelsImagenet(
              dataset.filenames(basename=True),
              real_json=args.real_labels,
          )
      else:
          real_labels = None
  
      crop_pct = 1.0 if test_time_pool else data_config["crop_pct"]
      loader = create_loader(
          dataset,
          input_size=data_config["input_size"],
          batch_size=args.batch_size,
          use_prefetcher=args.prefetcher,
          interpolation=data_config["interpolation"],
          mean=data_config["mean"],
          std=data_config["std"],
          num_workers=args.workers,
          crop_pct=crop_pct,
          pin_memory=args.pin_mem,
          tf_preprocessing=args.tf_preprocessing,
      )
  
      batch_time = AverageMeter()
      losses = AverageMeter()
      top1 = AverageMeter()
      top5 = AverageMeter()
  
      model.eval()
      with torch.no_grad():
          # warmup, reduce variability of first batch time, especially for comparing torchscript vs non
          input = torch.randn(
              (args.batch_size,) + tuple(data_config["input_size"])
          ).cuda()
          if args.channels_last:
              input = input.contiguous(
                  memory_format=torch.channels_last
              )
          model(input)
          end = time.time()
          for batch_idx, (input, target) in enumerate(loader):
              if args.no_prefetcher:
                  target = target.cuda()
                  input = input.cuda()
              if args.channels_last:
                  input = input.contiguous(
                      memory_format=torch.channels_last
                  )
  
              # compute output
              with amp_autocast():
                  output = model(input)
  
              if valid_labels is not None:
                  output = output[:, valid_labels]
              loss = criterion(output, target)
  
              if real_labels is not None:
                  real_labels.add_result(output)
  
              # measure accuracy and record loss
              acc1, acc5 = accuracy(
                  output.detach(), target, topk=(1, 5)
              )
              losses.update(loss.item(), input.size(0))
              top1.update(acc1.item(), input.size(0))
              top5.update(acc5.item(), input.size(0))
  
              # measure elapsed time
              batch_time.update(time.time() - end)
              end = time.time()
  
              if batch_idx % args.log_freq == 0:
                  _logger.info(
                      "Test: [{0:>4d}/{1}]  "
                      "Time: {batch_time.val:.3f}s ({batch_time.avg:.3f}s, {rate_avg:>7.2f}/s)  "
                      "Loss: {loss.val:>7.4f} ({loss.avg:>6.4f})  "
                      "Acc@1: {top1.val:>7.3f} ({top1.avg:>7.3f})  "
                      "Acc@5: {top5.val:>7.3f} ({top5.avg:>7.3f})".format(
                          batch_idx,
                          len(loader),
                          batch_time=batch_time,
                          rate_avg=input.size(0) / batch_time.avg,
                          loss=losses,
                          top1=top1,
                          top5=top5,
                      )
                  )
  
      if real_labels is not None:
          # real labels mode replaces topk values at the end
          top1a, top5a = real_labels.get_accuracy(
              k=1
          ), real_labels.get_accuracy(k=5)
      else:
          top1a, top5a = top1.avg, top5.avg
      results = OrderedDict(
          top1=round(top1a, 4),
          top1_err=round(100 - top1a, 4),
          top5=round(top5a, 4),
          top5_err=round(100 - top5a, 4),
          img_size=data_config["input_size"][-1],
          cropt_pct=crop_pct,
          interpolation=data_config["interpolation"],
      )
  
      _logger.info(
          " * Acc@1 {:.3f} ({:.3f}) Acc@5 {:.3f} ({:.3f})".format(
              results["top1"],
              results["top1_err"],
              results["top5"],
              results["top5_err"],
          )
      )
  
      return results
  
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 1 for calling a global function with example usage: validate(args) and returns: results