def fit(self,
train_data,
val_data=None,
train_size=0.9,
random_state=None,
time_limit=None):
"""Fit auto estimator given the input data.
Parameters
----------
train_data : pd.DataFrame or iterator
Training data.
val_data : pd.DataFrame or iterator, optional
Validation data, optional. If `train_data` is DataFrame, `val_data` will be split from
`train_data` given `train_size`.
train_size : float
The portion of train data split from original `train_data` if `val_data` is not provided.
random_state : int
Random state for splitting, for `np.random.seed`.
time_limit : int, default is None
The wall clock time limit(second) for fit process, if `None`, time limit is not enforced.
If `fit` takes longer than `time_limit`, the process will terminate early and return the
model prematurally.
Due to callbacks and additional validation functions, the `time_limit` may not be very precise
(few minutes allowance), but you can use it to safe-guard a very long training session.
If `time_limits` key set in __init__ with config, the `time_limit` value will overwrite configuration
if not `None`.
Returns
-------
Estimator
The estimator obtained by training on the specified dataset.
"""
config = self._config.copy()
if time_limit is None:
if config.get('time_limits', None):
time_limit = config['time_limits']
else:
time_limit = math.inf
elif not isinstance(time_limit, int):
raise TypeError(
f'Invalid type `time_limit={time_limit}`, int or None expected'
)
self.scheduler_options['time_out'] = time_limit
wall_clock_tick = time.time() + time_limit
# split train/val before HPO to make fair comparisons
if not isinstance(train_data, pd.DataFrame):
assert val_data is not None, \
"Please provide `val_data` as we do not know how to split `train_data` of type: \
{}" .format(type(train_data))
if val_data is None:
assert 0 <= train_size <= 1.0
if random_state:
np.random.seed(random_state)
split_mask = np.random.rand(len(train_data)) < train_size
train = train_data[split_mask]
val = train_data[~split_mask]
self._logger.info(
'Randomly split train_data into train[%d]/validation[%d] splits.',
len(train), len(val))
train_data, val_data = train, val
estimator = config.get('estimator', None)
if estimator is None:
estimator = [ImageClassificationEstimator]
else:
if isinstance(estimator, ag.Space):
estimator = estimator.data
elif isinstance(estimator, str):
estimator = [estimator]
for i, e in enumerate(estimator):
if e == 'img_cls':
estimator[i] = ImageClassificationEstimator
else:
estimator.pop(e)
if not estimator:
raise ValueError(
'Unable to determine the estimator for fit function.')
if len(estimator) == 1:
config['estimator'] = estimator[0]
else:
config['estimator'] = ag.Categorical(*estimator)
# register args
config['train_data'] = train_data
config['val_data'] = val_data
config['wall_clock_tick'] = wall_clock_tick
config['log_dir'] = os.path.join(config.get('log_dir', os.getcwd()),
str(uuid.uuid4())[:8])
_train_image_classification.register_args(**config)
start_time = time.time()
self._fit_summary = {}
self._results = {}
if config.get('num_trials', 1) < 2:
rand_config = RandomSearcher(
_train_image_classification.cs).get_config()
self._logger.info("Starting fit without HPO")
results = _train_image_classification(
_train_image_classification.args, rand_config)
best_config = sample_config(_train_image_classification.args,
rand_config)
best_config.pop('train_data', None)
best_config.pop('val_data', None)
self._fit_summary.update({
'train_acc':
results.get('train_acc', -1),
'valid_acc':
results.get('valid_acc', -1),
'total_time':
results.get('time',
time.time() - start_time),
'best_config':
best_config
})
self._results = self._fit_summary
else:
self._logger.info("Starting HPO experiments")
results = self.run_fit(_train_image_classification,
self.search_strategy,
self.scheduler_options)
if isinstance(results, dict):
ks = ('best_reward', 'best_config', 'total_time',
'config_history', 'reward_attr')
self._results.update(
{k: v
for k, v in results.items() if k in ks})
end_time = time.time()
self._logger.info("Finished, total runtime is %.2f s",
end_time - start_time)
if config.get('num_trials', 1) > 1:
best_config = sample_config(_train_image_classification.args,
results['best_config'])
# convert best config to nested form
best_config = config_to_nested(best_config)
best_config.pop('train_data', None)
best_config.pop('val_data', None)
self._fit_summary.update({
'train_acc':
results.get('train_acc', -1),
'valid_acc':
results.get('valid_acc', results.get('best_reward', -1)),
'total_time':
results.get('total_time',
time.time() - start_time),
'best_config':
best_config
})
self._logger.info(pprint.pformat(self._fit_summary, indent=2))
if self._cleanup_disk:
shutil.rmtree(config['log_dir'], ignore_errors=True)
model_checkpoint = results.get('model_checkpoint', None)
if model_checkpoint is None:
if results.get('traceback', '') == 'timeout':
raise TimeoutError(
f'Unable to fit a usable model given `time_limit={time_limit}`'
)
raise RuntimeError(
f'Unexpected error happened during fit: {pprint.pformat(results, indent=2)}'
)
estimator = pickle.loads(results['model_checkpoint'])
return estimator
def fit(self,
train_data,
val_data=None,
train_size=0.9,
random_state=None):
"""Fit auto estimator given the input data.
Parameters
----------
train_data : pd.DataFrame or iterator
Training data.
val_data : pd.DataFrame or iterator, optional
Validation data, optional. If `train_data` is DataFrame, `val_data` will be split from
`train_data` given `train_size`.
train_size : float
The portion of train data split from original `train_data` if `val_data` is not provided.
random_state : int
Random state for splitting, for `np.random.seed`.
Returns
-------
Estimator
The estimator obtained by training on the specified dataset.
"""
# split train/val before HPO to make fair comparisons
if not isinstance(train_data, pd.DataFrame):
assert val_data is not None, \
"Please provide `val_data` as we do not know how to split `train_data` of type: \
{}" .format(type(train_data))
if val_data is None:
assert 0 <= train_size <= 1.0
if random_state:
np.random.seed(random_state)
split_mask = np.random.rand(len(train_data)) < train_size
train = train_data[split_mask]
val = train_data[~split_mask]
self._logger.info(
'Randomly split train_data into train[%d]/validation[%d] splits.',
len(train), len(val))
train_data, val_data = train, val
# automatically suggest some hyperparameters based on the dataset statistics(experimental)
estimator = self._config.get('estimator', None)
if estimator is None:
estimator = [ImageClassificationEstimator]
self._config['estimator'] = ag.Categorical(*estimator)
# register args
config = self._config.copy()
config['train_data'] = train_data
config['val_data'] = val_data
_train_image_classification.register_args(**config)
start_time = time.time()
self._fit_summary = {}
if config.get('num_trials', 1) < 2:
rand_config = RandomSearcher(
_train_image_classification.cs).get_config()
self._logger.info("Starting fit without HPO")
results = _train_image_classification(
_train_image_classification.args, rand_config)
best_config = sample_config(_train_image_classification.args,
rand_config)
best_config.pop('train_data', None)
best_config.pop('val_data', None)
self._fit_summary.update({
'train_acc':
results.get('train_acc', -1),
'valid_acc':
results.get('valid_acc', -1),
'total_time':
results.get('time',
time.time() - start_time),
'best_config':
best_config
})
else:
self._logger.info("Starting HPO experiments")
results = self.run_fit(_train_image_classification,
self.search_strategy,
self.scheduler_options)
end_time = time.time()
self._logger.info(
">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> finish model fitting")
self._logger.info("total runtime is %.2f s", end_time - start_time)
if config.get('num_trials', 1) > 1:
best_config = sample_config(_train_image_classification.args,
results['best_config'])
# convert best config to nested form
best_config = config_to_nested(best_config)
best_config.pop('train_data', None)
best_config.pop('val_data', None)
self._fit_summary.update({
'train_acc':
results.get('train_acc', -1),
'valid_acc':
results.get('valid_acc', results.get('best_reward', -1)),
'total_time':
results.get('total_time',
time.time() - start_time),
'best_config':
best_config
})
self._logger.info(pprint.pformat(self._fit_summary, indent=2))
# TODO: checkpointing needs to be done in a better way
model_checkpoint = results.get('model_checkpoint', None)
if model_checkpoint is None:
raise RuntimeError(
f'Unexpected error happened during fit: {pprint.pformat(results, indent=2)}'
)
estimator = pickle.loads(results['model_checkpoint'])
return estimator
def fit(dataset='voc',
net=Categorical('mobilenet1.0'),
meta_arch='yolo3',
lr=Categorical(5e-4, 1e-4),
loss=gluon.loss.SoftmaxCrossEntropyLoss(),
split_ratio=0.8,
batch_size=16,
epochs=50,
num_trials=None,
time_limits=None,
nthreads_per_trial=12,
num_workers=32,
ngpus_per_trial=1,
hybridize=True,
scheduler_options=None,
search_strategy='random',
search_options=None,
verbose=False,
transfer='coco',
resume='',
checkpoint='checkpoint/exp1.ag',
visualizer='none',
dist_ip_addrs=None,
auto_search=True,
seed=223,
data_shape=416,
start_epoch=0,
lr_mode='step',
lr_decay=0.1,
lr_decay_period=0,
lr_decay_epoch='160,180',
warmup_lr=0.0,
warmup_epochs=2,
warmup_iters=1000,
warmup_factor=1. / 3.,
momentum=0.9,
wd=0.0005,
log_interval=100,
save_prefix='',
save_interval=10,
val_interval=1,
num_samples=-1,
no_random_shape=False,
no_wd=False,
mixup=False,
no_mixup_epochs=20,
label_smooth=False,
syncbn=False,
reuse_pred_weights=True,
**kwargs):
"""
Fit object detection models.
Parameters
----------
dataset : str or :class:`autogluon.task.ObjectDectection.Dataset`
Training dataset containing images and corresponding object bounding boxes.
net : str, :class:`autogluon.space.AutoGluonObject`
Which existing neural network base models to consider as candidates.
meta_arch : str
Meta architecture of the model. Currently support YoloV3 (Default) and FasterRCNN.
YoloV3 is faster, while FasterRCNN is more accurate.
lr : float or :class:`autogluon.space`
The learning rate to use in each update of the neural network weights during training.
loss : mxnet.gluon.loss
Loss function used during training of the neural network weights.
split_ratio : float
Fraction of dataset to hold-out during training in order to tune hyperparameters (i.e. validation data).
The final returned model may be fit to all of the data (after hyperparameters have been selected).
batch_size : int
How many images to group in each mini-batch during gradient computations in training.
epochs : int
How many epochs to train the neural networks for at most.
num_trials : int
Maximal number of hyperparameter configurations to try out.
time_limits : int
Approximately how long should `fit()` should run for (wallclock time in seconds).
`fit()` will stop training new models after this amount of time has elapsed (but models which have already started training will continue to completion).
nthreads_per_trial : int
How many CPUs to use in each trial (ie. single training run of a model).
num_workers : int
How many CPUs to use for data loading during training of a model.
ngpus_per_trial : int
How many GPUs to use in each trial (ie. single training run of a model).
hybridize : bool
Whether or not the MXNet neural network should be hybridized (for increased efficiency).
scheduler_options : dict
Extra arguments passed to __init__ of scheduler, to configure the
orchestration of training jobs during hyperparameter-tuning.
search_strategy : str
Which hyperparameter search algorithm to use.
Options include: 'random' (random search), 'skopt' (SKopt Bayesian
optimization), 'grid' (grid search), 'hyperband' (Hyperband random),
'rl' (reinforcement learner).
search_options : dict
Auxiliary keyword arguments to pass to the searcher that performs
hyperparameter optimization.
verbose : bool
Whether or not to print out intermediate information during training.
resume : str
Path to checkpoint file of existing model, from which model training
should resume.
If not empty, we also start the hyperparameter search from the state
loaded from checkpoint.
checkpoint : str or None
State of hyperparameter search is stored to this local file
visualizer : str
Describes method to visualize training progress during `fit()`. Options: ['mxboard', 'tensorboard', 'none'].
dist_ip_addrs : list
List of IP addresses corresponding to remote workers, in order to leverage distributed computation.
auto_search : bool
If True, enables automatic suggestion of network types and hyper-parameter ranges adaptively based on provided dataset.
seed : int
Random seed to set for reproducibility.
data_shape : int
Shape of the image data.
start_epoch : int
Which epoch we begin training from
(eg. if we resume training of an existing model, then this
argument may be set to the number of epochs the model has already been trained for previously).
lr_mode : str
What sort of learning rate schedule should be followed during training.
lr_decay : float
How much learning rate should be decayed during training.
lr_decay_period : int
How often learning rate should be decayed during training.
warmup_lr : float
Learning rate to use during warm up period at the start of training.
warmup_epochs : int
How many initial epochs constitute the "warm up" period of model training.
warmup_iters : int
How many initial iterations constitute the "warm up" period of model training.
This is used by R-CNNs
warmup_factor : float
warmup factor of target lr. initial lr starts from target lr * warmup_factor
momentum : float or :class:`autogluon.space`
Momentum to use in optimization of neural network weights during training.
wd : float or :class:`autogluon.space`
Weight decay to use in optimization of neural network weights during training.
log_interval : int
Log results every so many epochs during training.
save_prefix : str
Prefix to append to file name for saved model.
save_interval : int
Save a copy of model every so many epochs during training.
val_interval : int
Evaluate performance on held-out validation data every so many epochs during training.
no_random_shape : bool
Whether random shapes should not be used.
no_wd : bool
Whether weight decay should be turned off.
mixup : bool
Whether or not to utilize mixup data augmentation strategy.
no_mixup_epochs : int
If using mixup, we first train model for this many epochs without mixup data augmentation.
label_smooth : bool
Whether or not to utilize label smoothing.
syncbn : bool
Whether or not to utilize synchronized batch normalization.
Returns
-------
:class:`autogluon.task.object_detection.Detector` object which can make predictions on new data and summarize what happened during `fit()`.
Examples
--------
>>> from autogluon.vision.object_detection import ObjectDetection as task
>>> detector = task.fit(dataset = 'voc', net = 'mobilenet1.0',
>>> time_limits = 600, ngpus_per_trial = 1, num_trials = 1)
"""
assert search_strategy not in {'bayesopt', 'bayesopt_hyperband'}, \
"search_strategy == 'bayesopt' or 'bayesopt_hyperband' not yet supported"
if auto_search:
# The strategies can be injected here, for example: automatic suggest some hps
# based on the dataset statistics
pass
nthreads_per_trial = get_cpu_count(
) if nthreads_per_trial > get_cpu_count() else nthreads_per_trial
if ngpus_per_trial > get_gpu_count():
logger.warning(
"The number of requested GPUs is greater than the number of available GPUs."
)
ngpus_per_trial = get_gpu_count(
) if ngpus_per_trial > get_gpu_count() else ngpus_per_trial
# If only time_limits is given, the scheduler starts trials until the
# time limit is reached
if num_trials is None and time_limits is None:
num_trials = 2
train_object_detection.register_args(
meta_arch=meta_arch,
dataset=dataset,
net=net,
lr=lr,
loss=loss,
num_gpus=ngpus_per_trial,
batch_size=batch_size,
split_ratio=split_ratio,
epochs=epochs,
num_workers=nthreads_per_trial,
hybridize=hybridize,
verbose=verbose,
final_fit=False,
seed=seed,
data_shape=data_shape,
start_epoch=0,
transfer=transfer,
lr_mode=lr_mode,
lr_decay=lr_decay,
lr_decay_period=lr_decay_period,
lr_decay_epoch=lr_decay_epoch,
warmup_lr=warmup_lr,
warmup_epochs=warmup_epochs,
warmup_iters=warmup_iters,
warmup_factor=warmup_factor,
momentum=momentum,
wd=wd,
log_interval=log_interval,
save_prefix=save_prefix,
save_interval=save_interval,
val_interval=val_interval,
num_samples=num_samples,
no_random_shape=no_random_shape,
no_wd=no_wd,
mixup=mixup,
no_mixup_epochs=no_mixup_epochs,
label_smooth=label_smooth,
resume=resume,
syncbn=syncbn,
reuse_pred_weights=reuse_pred_weights)
# Backward compatibility:
grace_period = kwargs.get('grace_period')
if grace_period is not None:
if scheduler_options is None:
scheduler_options = {'grace_period': grace_period}
else:
assert 'grace_period' not in scheduler_options, \
"grace_period appears both in scheduler_options and as direct argument"
scheduler_options = copy.copy(scheduler_options)
scheduler_options['grace_period'] = grace_period
logger.warning("grace_period is deprecated, use "
"scheduler_options={'grace_period': ...} instead")
scheduler_options = compile_scheduler_options(
scheduler_options=scheduler_options,
search_strategy=search_strategy,
search_options=search_options,
nthreads_per_trial=nthreads_per_trial,
ngpus_per_trial=ngpus_per_trial,
checkpoint=checkpoint,
num_trials=num_trials,
time_out=time_limits,
resume=(len(resume) > 0),
visualizer=visualizer,
time_attr='epoch',
reward_attr='map_reward',
dist_ip_addrs=dist_ip_addrs,
epochs=epochs)
results = BaseTask.run_fit(train_object_detection, search_strategy,
scheduler_options)
logger.info(">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> finish model fitting")
args = sample_config(train_object_detection.args,
results['best_config'])
logger.info('The best config: {}'.format(results['best_config']))
ngpus = get_gpu_count()
ctx = [mx.gpu(i) for i in range(ngpus)] if ngpus > 0 else [mx.cpu()]
model = get_network(args.meta_arch,
args.net,
dataset.init().get_classes(),
transfer,
ctx,
syncbn=args.syncbn)
update_params(model, results.pop('model_params'))
return Detector(model, results, checkpoint, args)
