def parse_policy(self, hparams):
"""Parses policy schedule from input, which can be a list, list of lists, text file, or pickled list.
If list is not nested, then uses the same policy for all epochs.
Args:
hparams: tf.hparams object.
"""
# Parse policy
if isinstance(hparams.hp_policy,
str) and hparams.hp_policy.endswith('.txt'):
if hparams.num_epochs % hparams.hp_policy_epochs != 0:
tf.logging.warning(
"Schedule length (%s) doesn't divide evenly into epochs (%s), interpolating.",
hparams.num_epochs, hparams.hp_policy_epochs)
tf.logging.info(
'schedule policy trained on {} epochs, parsing from: {}, multiplier: {}'
.format(hparams.hp_policy_epochs, hparams.hp_policy,
float(hparams.num_epochs) / hparams.hp_policy_epochs))
raw_policy = parse_log_schedule(
hparams.hp_policy,
epochs=hparams.hp_policy_epochs,
multiplier=float(hparams.num_epochs) /
hparams.hp_policy_epochs)
elif isinstance(hparams.hp_policy, list):
# support list of hp_policy for search stage
raw_policy = hparams.hp_policy
else:
raise ValueError('hp_policy must be txt or None during training!')
if isinstance(raw_policy[0], list):
self.policy = []
split = len(raw_policy[0]) // 2
for pol in raw_policy:
cur_pol = self._parse_policy(pol[:split])
cur_pol.extend(self._parse_policy(pol[split:]))
self.policy.append(cur_pol)
tf.logging.info('using HP policy schedule, last: {}'.format(
self.policy[-1]))
elif isinstance(raw_policy, list):
split = len(raw_policy) // 2
self.policy = self._parse_policy(raw_policy[:split])
self.policy.extend(self._parse_policy(raw_policy[split:]))
tf.logging.info('using HP Policy, policy: {}'.format(self.policy))
def parse_policy(self, hparams):
"""Parses policy schedule from input, which can be a list, list of lists, text file, or pickled list.
If list is not nested, then uses the same policy for all epochs.
Args:
hparams: tf.hparams object.
"""
# Parse policy
if hparams.use_hp_policy:
self.augmentation_transforms = augmentation_transforms_pba
if isinstance(hparams.hp_policy,
str) and hparams.hp_policy.endswith('.txt'):
if hparams.num_epochs % hparams.hp_policy_epochs != 0:
tf.logging.warning(
"Schedule length (%s) doesn't divide evenly into epochs (%s), interpolating.",
hparams.num_epochs, hparams.hp_policy_epochs)
tf.logging.info(
'schedule policy trained on {} epochs, parsing from: {}, multiplier: {}'
.format(
hparams.hp_policy_epochs, hparams.hp_policy,
float(hparams.num_epochs) / hparams.hp_policy_epochs))
raw_policy = parse_log_schedule(
hparams.hp_policy,
epochs=hparams.hp_policy_epochs,
multiplier=float(hparams.num_epochs) /
hparams.hp_policy_epochs)
elif isinstance(hparams.hp_policy,
str) and hparams.hp_policy.endswith('.p'):
assert hparams.num_epochs % hparams.hp_policy_epochs == 0
tf.logging.info('custom .p file, policy number: {}'.format(
hparams.schedule_num))
with open(hparams.hp_policy, 'rb') as f:
policy = pickle.load(f)[hparams.schedule_num]
raw_policy = []
for num_iters, pol in policy:
for _ in range(num_iters * hparams.num_epochs //
hparams.hp_policy_epochs):
raw_policy.append(pol)
else:
raw_policy = hparams.hp_policy
if isinstance(raw_policy[0], list):
self.policy = []
split = len(raw_policy[0]) // 2
for pol in raw_policy:
cur_pol = parse_policy(pol[:split],
self.augmentation_transforms)
cur_pol.extend(
parse_policy(pol[split:],
self.augmentation_transforms))
self.policy.append(cur_pol)
tf.logging.info('using HP policy schedule, last: {}'.format(
self.policy[-1]))
elif isinstance(raw_policy, list):
split = len(raw_policy) // 2
self.policy = parse_policy(raw_policy[:split],
self.augmentation_transforms)
self.policy.extend(
parse_policy(raw_policy[split:],
self.augmentation_transforms))
tf.logging.info('using HP Policy, policy: {}'.format(
self.policy))
else:
self.augmentation_transforms = augmentation_transforms_autoaug
tf.logging.info('using ENAS Policy or no augmentaton policy')
if 'svhn' in hparams.dataset:
self.good_policies = found_policies.good_policies_svhn()
else:
assert 'cifar' in hparams.dataset
self.good_policies = found_policies.good_policies()
def parse_policy(self, hparams):
"""Parses policy schedule from input, which can be a list, list of lists, text file, or pickled list.
If list is not nested, then uses the same policy for all epochs.
Args:
hparams: tf.hparams object.
"""
if hparams.no_aug_policy:
tf.logging.info("no augmentation policy will be used")
if hparams.use_kitti_aug:
tf.logging.info("using augmentations from SIGNet")
return
# Parse policy
if hparams.use_hp_policy:
self.augmentation_transforms = augmentation_transforms_pba
tf.logging.info('hp policy is selected')
if isinstance(hparams.hp_policy, str) and hparams.hp_policy.endswith('.txt'):
if hparams.num_epochs % hparams.hp_policy_epochs != 0:
tf.logging.warning(
"Schedule length (%s) doesn't divide evenly into epochs (%s), interpolating.",
hparams.num_epochs, hparams.hp_policy_epochs
)
tf.logging.info(
'schedule policy trained on {} epochs, parsing from: {}, multiplier: {}'.format(
hparams.hp_policy_epochs, hparams.hp_policy,
float(hparams.num_epochs) / hparams.hp_policy_epochs
)
)
raw_policy = parse_log_schedule(
hparams.hp_policy,
epochs=hparams.hp_policy_epochs,
multiplier=float(hparams.num_epochs) / hparams.hp_policy_epochs
)
else:
raw_policy = hparams.hp_policy
if isinstance(raw_policy[0], list):
self.policy = []
split = len(raw_policy[0]) // 2
for pol in raw_policy:
cur_pol = parse_policy(pol[:split], self.augmentation_transforms)
cur_pol.extend(parse_policy(pol[split:], self.augmentation_transforms))
self.policy.append(cur_pol)
tf.logging.info('using HP policy schedule, last: {}'.format(self.policy[-1]))
if self.comet_exp is not None:
self.comet_exp.log_parameter('hp_policy_schedule_last', self.policy[-1])
elif isinstance(raw_policy, list):
split = len(raw_policy) // 2
self.policy = parse_policy(raw_policy[:split], self.augmentation_transforms)
self.policy.extend(parse_policy(raw_policy[split:], self.augmentation_transforms))
tf.logging.info('using HP Policy, policy: {}'.format(self.policy))
if self.comet_exp is not None:
self.comet_exp.log_parameter('hp_policy', self.policy)
else:
# use autoaugment policies modified for KITTI
self.augmentation_transforms = augmentation_transforms_autoaug
tf.logging.info('using autoaument policy: {}'.format(hparams.policy_dataset))
if hparams.policy_dataset == 'svhn':
self.good_policies = found_policies.good_policies_svhn()
else:
# use cifar10 good policies
self.good_policies = found_policies.good_policies_cifar()
