models/bls2017.py [370:444]:
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      help="Path where to log training metrics for TensorBoard and back up "
           "intermediate model checkpoints.")
  train_cmd.add_argument(
      "--batchsize", type=int, default=8,
      help="Batch size for training and validation.")
  train_cmd.add_argument(
      "--patchsize", type=int, default=256,
      help="Size of image patches for training and validation.")
  train_cmd.add_argument(
      "--epochs", type=int, default=1000,
      help="Train up to this number of epochs. (One epoch is here defined as "
           "the number of steps given by --steps_per_epoch, not iterations "
           "over the full training dataset.)")
  train_cmd.add_argument(
      "--steps_per_epoch", type=int, default=1000,
      help="Perform validation and produce logs after this many batches.")
  train_cmd.add_argument(
      "--max_validation_steps", type=int, default=16,
      help="Maximum number of batches to use for validation. If -1, use one "
           "patch from each image in the training set.")
  train_cmd.add_argument(
      "--preprocess_threads", type=int, default=16,
      help="Number of CPU threads to use for parallel decoding of training "
           "images.")
  train_cmd.add_argument(
      "--check_numerics", action="store_true",
      help="Enable TF support for catching NaN and Inf in tensors.")

  # 'compress' subcommand.
  compress_cmd = subparsers.add_parser(
      "compress",
      formatter_class=argparse.ArgumentDefaultsHelpFormatter,
      description="Reads a PNG file, compresses it, and writes a TFCI file.")

  # 'decompress' subcommand.
  decompress_cmd = subparsers.add_parser(
      "decompress",
      formatter_class=argparse.ArgumentDefaultsHelpFormatter,
      description="Reads a TFCI file, reconstructs the image, and writes back "
                  "a PNG file.")

  # Arguments for both 'compress' and 'decompress'.
  for cmd, ext in ((compress_cmd, ".tfci"), (decompress_cmd, ".png")):
    cmd.add_argument(
        "input_file",
        help="Input filename.")
    cmd.add_argument(
        "output_file", nargs="?",
        help=f"Output filename (optional). If not provided, appends '{ext}' to "
             f"the input filename.")

  # Parse arguments.
  args = parser.parse_args(argv[1:])
  if args.command is None:
    parser.print_usage()
    sys.exit(2)
  return args


def main(args):
  # Invoke subcommand.
  if args.command == "train":
    train(args)
  elif args.command == "compress":
    if not args.output_file:
      args.output_file = args.input_file + ".tfci"
    compress(args)
  elif args.command == "decompress":
    if not args.output_file:
      args.output_file = args.input_file + ".png"
    decompress(args)


if __name__ == "__main__":
  app.run(main, flags_parser=parse_args)
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models/bmshj2018.py [455:529]:
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      help="Path where to log training metrics for TensorBoard and back up "
           "intermediate model checkpoints.")
  train_cmd.add_argument(
      "--batchsize", type=int, default=8,
      help="Batch size for training and validation.")
  train_cmd.add_argument(
      "--patchsize", type=int, default=256,
      help="Size of image patches for training and validation.")
  train_cmd.add_argument(
      "--epochs", type=int, default=1000,
      help="Train up to this number of epochs. (One epoch is here defined as "
           "the number of steps given by --steps_per_epoch, not iterations "
           "over the full training dataset.)")
  train_cmd.add_argument(
      "--steps_per_epoch", type=int, default=1000,
      help="Perform validation and produce logs after this many batches.")
  train_cmd.add_argument(
      "--max_validation_steps", type=int, default=16,
      help="Maximum number of batches to use for validation. If -1, use one "
           "patch from each image in the training set.")
  train_cmd.add_argument(
      "--preprocess_threads", type=int, default=16,
      help="Number of CPU threads to use for parallel decoding of training "
           "images.")
  train_cmd.add_argument(
      "--check_numerics", action="store_true",
      help="Enable TF support for catching NaN and Inf in tensors.")

  # 'compress' subcommand.
  compress_cmd = subparsers.add_parser(
      "compress",
      formatter_class=argparse.ArgumentDefaultsHelpFormatter,
      description="Reads a PNG file, compresses it, and writes a TFCI file.")

  # 'decompress' subcommand.
  decompress_cmd = subparsers.add_parser(
      "decompress",
      formatter_class=argparse.ArgumentDefaultsHelpFormatter,
      description="Reads a TFCI file, reconstructs the image, and writes back "
                  "a PNG file.")

  # Arguments for both 'compress' and 'decompress'.
  for cmd, ext in ((compress_cmd, ".tfci"), (decompress_cmd, ".png")):
    cmd.add_argument(
        "input_file",
        help="Input filename.")
    cmd.add_argument(
        "output_file", nargs="?",
        help=f"Output filename (optional). If not provided, appends '{ext}' to "
             f"the input filename.")

  # Parse arguments.
  args = parser.parse_args(argv[1:])
  if args.command is None:
    parser.print_usage()
    sys.exit(2)
  return args


def main(args):
  # Invoke subcommand.
  if args.command == "train":
    train(args)
  elif args.command == "compress":
    if not args.output_file:
      args.output_file = args.input_file + ".tfci"
    compress(args)
  elif args.command == "decompress":
    if not args.output_file:
      args.output_file = args.input_file + ".png"
    decompress(args)


if __name__ == "__main__":
  app.run(main, flags_parser=parse_args)
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