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https://github.com/AntoineHX/smart_augmentation.git
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Premieres modifications pour LeNet sur PBA
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Harle, Antoine (Contracteur)
parent
67f99f3354
commit
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5 changed files with 684 additions and 1 deletions
210
PBA/setup.py
Executable file
210
PBA/setup.py
Executable file
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"""Parse flags and set up hyperparameters."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import argparse
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import random
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import tensorflow as tf
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from pba.augmentation_transforms_hp import NUM_HP_TRANSFORM
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def create_parser(state):
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"""Create arg parser for flags."""
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parser = argparse.ArgumentParser()
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parser.add_argument(
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'--model_name',
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default='wrn',
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choices=('wrn_28_10', 'wrn_40_2', 'shake_shake_32', 'shake_shake_96',
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'shake_shake_112', 'pyramid_net', 'resnet', 'LeNet'))
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parser.add_argument(
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'--data_path',
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default='/tmp/datasets/',
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help='Directory where dataset is located.')
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parser.add_argument(
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'--dataset',
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default='cifar10',
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choices=('cifar10', 'cifar100', 'svhn', 'svhn-full', 'test'))
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parser.add_argument(
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'--recompute_dset_stats',
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action='store_true',
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help='Instead of using hardcoded mean/std, recompute from dataset.')
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parser.add_argument('--local_dir', type=str, default='/tmp/ray_results/', help='Ray directory.')
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parser.add_argument('--restore', type=str, default=None, help='If specified, tries to restore from given path.')
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parser.add_argument('--train_size', type=int, default=5000, help='Number of training examples.')
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parser.add_argument('--val_size', type=int, default=45000, help='Number of validation examples.')
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parser.add_argument('--checkpoint_freq', type=int, default=50, help='Checkpoint frequency.')
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parser.add_argument(
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'--cpu', type=float, default=4, help='Allocated by Ray')
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parser.add_argument(
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'--gpu', type=float, default=1, help='Allocated by Ray')
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parser.add_argument(
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'--aug_policy',
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type=str,
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default='cifar10',
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help=
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'which augmentation policy to use (in augmentation_transforms_hp.py)')
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# search-use only
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parser.add_argument(
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'--explore',
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type=str,
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default='cifar10',
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help='which explore function to use')
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parser.add_argument(
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'--epochs',
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type=int,
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default=0,
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help='Number of epochs, or <=0 for default')
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parser.add_argument(
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'--no_cutout', action='store_true', help='turn off cutout')
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parser.add_argument('--lr', type=float, default=0.1, help='learning rate')
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parser.add_argument('--wd', type=float, default=0.0005, help='weight decay')
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parser.add_argument('--bs', type=int, default=128, help='batch size')
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parser.add_argument('--test_bs', type=int, default=25, help='test batch size')
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parser.add_argument('--num_samples', type=int, default=1, help='Number of Ray samples')
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if state == 'train':
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parser.add_argument(
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'--use_hp_policy',
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action='store_true',
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help='otherwise use autoaug policy')
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parser.add_argument(
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'--hp_policy',
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type=str,
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default=None,
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help='either a comma separated list of values or a file')
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parser.add_argument(
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'--hp_policy_epochs',
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type=int,
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default=200,
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help='number of epochs/iterations policy trained for')
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parser.add_argument(
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'--no_aug',
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action='store_true',
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help=
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'no additional augmentation at all (besides cutout if not toggled)'
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)
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parser.add_argument(
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'--flatten',
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action='store_true',
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help='randomly select aug policy from schedule')
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parser.add_argument('--name', type=str, default='autoaug')
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elif state == 'search':
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parser.add_argument('--perturbation_interval', type=int, default=10)
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parser.add_argument('--name', type=str, default='autoaug_pbt')
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else:
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raise ValueError('unknown state')
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args = parser.parse_args()
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tf.logging.info(str(args))
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return args
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def create_hparams(state, FLAGS): # pylint: disable=invalid-name
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"""Creates hyperparameters to pass into Ray config.
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Different options depending on search or eval mode.
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Args:
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state: a string, 'train' or 'search'.
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FLAGS: parsed command line flags.
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Returns:
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tf.hparams object.
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"""
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epochs = 0
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tf.logging.info('data path: {}'.format(FLAGS.data_path))
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hparams = tf.contrib.training.HParams(
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train_size=FLAGS.train_size,
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validation_size=FLAGS.val_size,
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dataset=FLAGS.dataset,
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data_path=FLAGS.data_path,
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batch_size=FLAGS.bs,
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gradient_clipping_by_global_norm=5.0,
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explore=FLAGS.explore,
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aug_policy=FLAGS.aug_policy,
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no_cutout=FLAGS.no_cutout,
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recompute_dset_stats=FLAGS.recompute_dset_stats,
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lr=FLAGS.lr,
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weight_decay_rate=FLAGS.wd,
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test_batch_size=FLAGS.test_bs)
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if state == 'train':
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hparams.add_hparam('no_aug', FLAGS.no_aug)
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hparams.add_hparam('use_hp_policy', FLAGS.use_hp_policy)
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if FLAGS.use_hp_policy:
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if FLAGS.hp_policy == 'random':
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tf.logging.info('RANDOM SEARCH')
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parsed_policy = []
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for i in range(NUM_HP_TRANSFORM * 4):
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if i % 2 == 0:
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parsed_policy.append(random.randint(0, 10))
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else:
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parsed_policy.append(random.randint(0, 9))
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elif FLAGS.hp_policy.endswith('.txt') or FLAGS.hp_policy.endswith(
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'.p'):
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# will be loaded in in data_utils
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parsed_policy = FLAGS.hp_policy
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else:
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# parse input into a fixed augmentation policy
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parsed_policy = FLAGS.hp_policy.split(', ')
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parsed_policy = [int(p) for p in parsed_policy]
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hparams.add_hparam('hp_policy', parsed_policy)
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hparams.add_hparam('hp_policy_epochs', FLAGS.hp_policy_epochs)
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hparams.add_hparam('flatten', FLAGS.flatten)
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elif state == 'search':
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hparams.add_hparam('no_aug', False)
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hparams.add_hparam('use_hp_policy', True)
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# default start value of 0
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hparams.add_hparam('hp_policy',
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[0 for _ in range(4 * NUM_HP_TRANSFORM)])
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else:
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raise ValueError('unknown state')
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if FLAGS.model_name == 'wrn_40_2':
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hparams.add_hparam('model_name', 'wrn')
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epochs = 200
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hparams.add_hparam('wrn_size', 32)
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hparams.add_hparam('wrn_depth', 40)
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elif FLAGS.model_name == 'wrn_28_10':
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hparams.add_hparam('model_name', 'wrn')
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epochs = 200
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hparams.add_hparam('wrn_size', 160)
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hparams.add_hparam('wrn_depth', 28)
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elif FLAGS.model_name == 'resnet':
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hparams.add_hparam('model_name', 'resnet')
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epochs = 200
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hparams.add_hparam('resnet_size', 20)
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hparams.add_hparam('num_filters', 32)
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elif FLAGS.model_name == 'shake_shake_32':
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hparams.add_hparam('model_name', 'shake_shake')
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epochs = 1800
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hparams.add_hparam('shake_shake_widen_factor', 2)
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elif FLAGS.model_name == 'shake_shake_96':
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hparams.add_hparam('model_name', 'shake_shake')
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epochs = 1800
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hparams.add_hparam('shake_shake_widen_factor', 6)
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elif FLAGS.model_name == 'shake_shake_112':
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hparams.add_hparam('model_name', 'shake_shake')
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epochs = 1800
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hparams.add_hparam('shake_shake_widen_factor', 7)
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elif FLAGS.model_name == 'pyramid_net':
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hparams.add_hparam('model_name', 'pyramid_net')
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epochs = 1800
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hparams.set_hparam('batch_size', 64)
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elif FLAGS.model_name == 'LeNet':
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hparams.add_hparam('model_name', 'LeNet')
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epochs = 200
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else:
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raise ValueError('Not Valid Model Name: %s' % FLAGS.model_name)
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if FLAGS.epochs > 0:
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tf.logging.info('overwriting with custom epochs')
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epochs = FLAGS.epochs
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hparams.add_hparam('num_epochs', epochs)
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tf.logging.info('epochs: {}, lr: {}, wd: {}'.format(
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hparams.num_epochs, hparams.lr, hparams.weight_decay_rate))
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return hparams
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