text: # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
  r"""
  Basic training script for PyTorch
  """
  
  # Set up custom environment before nearly anything else is imported
  # NOTE: this should be the first import (no not reorder)
  from maskrcnn_benchmark.utils.env import setup_environment  # noqa F401 isort:skip
  
  import argparse
  import os
  
  import torch
  import torch.utils.data
  from maskrcnn_benchmark.config import cfg
  from maskrcnn_benchmark.solver import make_lr_scheduler
  from maskrcnn_benchmark.solver import make_optimizer
  from maskrcnn_benchmark.data.datasets.egohands import EgohandsDataset
  from maskrcnn_benchmark.data.transforms.build import build_transforms
  from maskrcnn_benchmark.data.build import make_batch_data_sampler
  from maskrcnn_benchmark.data.collate_batch import BatchCollator, BBoxAugCollator
  from maskrcnn_benchmark.engine.inference import inference
  from maskrcnn_benchmark.engine.trainer import do_train
  from maskrcnn_benchmark.modeling.detector import build_detection_model
  from maskrcnn_benchmark.utils.checkpoint import DetectronCheckpointer
  from maskrcnn_benchmark.utils.collect_env import collect_env_info
  from maskrcnn_benchmark.utils.comm import synchronize, get_rank
  from maskrcnn_benchmark.utils.imports import import_file
  from maskrcnn_benchmark.utils.logger import setup_logger
  from maskrcnn_benchmark.utils.miscellaneous import mkdir
  
  # See if we can use apex.DistributedDataParallel instead of the torch default,
  # and enable mixed-precision via apex.amp
  try:
      from apex import amp
  except ImportError:
      raise ImportError('Use APEX for multi-precision via apex.amp')
  
  
  def run_test():
      torch.cuda.empty_cache()
      iou_types = ("bbox",)
  
  
  
  transforms = build_transforms(cfg)
  dataset = EgohandsDataset([0, 1, 2], [0, 1, 2], [0, 1, 2, 3], [0, 1, 2, 3], transforms)
  num_iters = 70000
  images_per_gpu = 1
  start_iter = 0
  sampler = torch.utils.data.sampler.RandomSampler(dataset)
  batch_sampler = make_batch_data_sampler(dataset, sampler, False, images_per_gpu, num_iters, start_iter)
  collator = BBoxAugCollator() if not True and cfg.TEST.BBOX_AUG.ENABLED else \
      BatchCollator(cfg.DATALOADER.SIZE_DIVISIBILITY)
  num_workers = cfg.DATALOADER.NUM_WORKERS
  data_loader = torch.utils.data.DataLoader(
      dataset,
      num_workers=num_workers,
      batch_sampler=batch_sampler,
      collate_fn=collator,
  )
  
  model = build_detection_model(cfg)
  device = torch.device(cfg.MODEL.DEVICE)
  model.to(device)
  
  optimizer = make_optimizer(cfg, model)
  scheduler = make_lr_scheduler(cfg, optimizer)
  
  use_mixed_precision = cfg.DTYPE == "float16"
  amp_opt_level = 'O1' if use_mixed_precision else 'O0'
  model, optimizer = amp.initialize(model, optimizer, opt_level=amp_opt_level)
  
  arguments = {}
  arguments["iteration"] = 0
  
  output_dir = "/home/lab/github/maskrcnn-benchmark/egohands_output"
  
  logger = setup_logger("maskrcnn_benchmark", output_dir, get_rank())
  # logger.info("Using {} GPUs".format(num_gpus))
  # logger.info(args)
  #
  # logger.info("Collecting env info (might take some time)")
  # logger.info("\n" + collect_env_info())
  #
  # logger.info("Loaded configuration file {}".format(args.config_file))
  # with open(args.config_file, "r") as cf:
  #     config_str = "\n" + cf.read()
  #     logger.info(config_str)
  logger.info("Running with config:\n{}".format(cfg))
  
  save_to_disk = get_rank() == 0
  checkpointer = DetectronCheckpointer(
      cfg, model, optimizer, scheduler, output_dir, save_to_disk
  )
  extra_checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT)
  arguments.update(extra_checkpoint_data)
  
  checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
  
  do_train(
          model,
          data_loader,
          optimizer,
          scheduler,
          checkpointer,
          device,
          checkpoint_period,
          arguments,
      )
  