Python get_output_tb_dir Examples

Example #1

File: trainval_net.py Project: crim-ca/RGB-N

  print('Using config:')
  pprint.pprint(cfg)

  np.random.seed(cfg.RNG_SEED)

  # train set
  imdb, roidb = combined_roidb(args.imdb_name)
  print('{:d} roidb entries'.format(len(roidb)))

  # output directory where the models are saved
  output_dir = get_output_dir(imdb, args.tag)
  print('Output will be saved to `{:s}`'.format(output_dir))

  # tensorboard directory where the summaries are saved during training
  tb_dir = get_output_tb_dir(imdb, args.tag)
  print('TensorFlow summaries will be saved to `{:s}`'.format(tb_dir))

  # also add the validation set, but with no flipping images
  orgflip = cfg.TRAIN.USE_FLIPPED
  orgnoise=cfg.TRAIN.USE_NOISE_AUG
  orgjpg=cfg.TRAIN.USE_JPG_AUG
  cfg.TRAIN.USE_FLIPPED = False
  cfg.TRAIN.USE_NOISE_AUG=False
  cfg.TRAIN.USE_JPG_AUG=False
  _, valroidb = combined_roidb(args.imdbval_name)
  print('{:d} validation roidb entries'.format(len(valroidb)))
  cfg.TRAIN.USE_FLIPPED = orgflip
  cfg.TRAIN.USE_NOISE_AUG = orgnoise
  cfg.TRAIN.USE_JPG_AUG=orgjpg

Example #2

File: trainval_net.py Project: sunshinezhihuo/AlphaPose

  print('Using config:')
  pprint.pprint(cfg)

  np.random.seed(cfg.RNG_SEED)

  # train set
  imdb, roidb = combined_roidb(args.imdb_name)
  print('{:d} roidb entries'.format(len(roidb)))

  # output directory where the models are saved
  output_dir = get_output_dir(imdb, args.tag)
  print('Output will be saved to `{:s}`'.format(output_dir))

  # tensorboard directory where the summaries are saved during training
  tb_dir = get_output_tb_dir(imdb, args.tag)
  print('TensorFlow summaries will be saved to `{:s}`'.format(tb_dir))

  # also add the validation set, but with no flipping images
  orgflip = cfg.TRAIN.USE_FLIPPED
  cfg.TRAIN.USE_FLIPPED = False
  _, valroidb = combined_roidb(args.imdbval_name)
  print('{:d} validation roidb entries'.format(len(valroidb)))
  cfg.TRAIN.USE_FLIPPED = orgflip

  # load network
  if args.net == 'vgg16':
    net = vgg16()
  elif args.net == 'res50':
    net = resnetv1(num_layers=50)
  elif args.net == 'res101':

Example #3

    print('Using config:')
    pprint.pprint(cfg)

    np.random.seed(cfg.RNG_SEED)

    # train set
    imdb, roidb = combined_roidb(imdb_name)
    print('{:d} roidb entries'.format(len(roidb)))

    # output directory where the models are saved
    output_dir = get_output_dir(imdb, tag)
    print('Output will be saved to `{:s}`'.format(output_dir))

    # tensorboard directory where the summaries are saved during training
    tb_dir = get_output_tb_dir(imdb, tag)
    print('TensorFlow summaries will be saved to `{:s}`'.format(tb_dir))

    # also add the validation set, but with no flipping images
    orgflip = cfg.TRAIN.USE_FLIPPED
    cfg.TRAIN.USE_FLIPPED = True
    _, valroidb = combined_roidb(imdbval_name)
    print('{:d} validation roidb entries'.format(len(valroidb)))
    cfg.TRAIN.USE_FLIPPED = orgflip

    # load network
    if net == 'vgg16':
        net = vgg16()
    elif net == 'res50':
        net = resnetv1(num_layers=50)
    elif net == 'res101':