def load_state_dict(self, state_dict):
update_params(self.supernet,
state_dict['supernet_params'],
ctx=self.ctx)
update_params(self.controller,
state_dict['controller_params'],
ctx=self.controller.context)
self.training_history = state_dict['training_history']
def load(cls, checkpoint):
""" load trained object detector from the file specified by 'checkpoint'
"""
state_dict = load(checkpoint)
args = state_dict['args']
results = pkl.loads(state_dict['results'])
scheduler_checkpoint = state_dict['scheduler_checkpoint']
model_params = state_dict['model_params']
classes = state_dict['classes']
model = get_network(args.meta_arch,
args.net,
classes,
ctx=mx.cpu(0),
syncbn=args.syncbn)
update_params(model, model_params)
return cls(model,
results,
scheduler_checkpoint,
args,
format_results=False)
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, default = None
Which hyperparameter search algorithm to use. Options include:
'random' (random search), 'bayesopt' (Gaussian process Bayesian optimization),
'skopt' (SKopt Bayesian optimization), 'grid' (grid search),
'hyperband' (Hyperband scheduling with random search), 'bayesopt-hyperband'
(Hyperband scheduling with GP-BO search).
If unspecified, the default is 'random'.
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)
"""
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)
def fit(dataset,
net=ag.Categorical('ResNet50_v1b', 'ResNet18_v1b'),
optimizer=NAG(learning_rate=ag.Real(1e-3, 1e-2, log=True),
wd=ag.Real(1e-4, 1e-3, log=True),
multi_precision=False),
loss=SoftmaxCrossEntropyLoss(),
split_ratio=0.8,
batch_size=64,
input_size=224,
epochs=20,
final_fit_epochs=None,
ensemble=1,
metric='accuracy',
nthreads_per_trial=60,
ngpus_per_trial=1,
hybridize=True,
scheduler_options=None,
search_strategy='random',
search_options=None,
plot_results=False,
verbose=False,
num_trials=None,
time_limits=None,
resume=False,
output_directory='checkpoint/',
visualizer='none',
dist_ip_addrs=None,
auto_search=True,
lr_config=ag.Dict(lr_mode='cosine',
lr_decay=0.1,
lr_decay_period=0,
lr_decay_epoch='40,80',
warmup_lr=0.0,
warmup_epochs=0),
tricks=ag.Dict(last_gamma=False,
use_pretrained=True,
use_se=False,
mixup=False,
mixup_alpha=0.2,
mixup_off_epoch=0,
label_smoothing=False,
no_wd=False,
teacher_name=None,
temperature=20.0,
hard_weight=0.5,
batch_norm=False,
use_gn=False),
**kwargs):
# TODO: ensemble and hybridize are not in docstring
"""
Fit image classification models to a given dataset.
Parameters
----------
dataset : str or :meth:`autogluon.task.ImageClassification.Dataset`
Training dataset containing images and their associated class labels.
Popular image datasets built into AutoGluon can be used by specifying their name as a string (options: ‘mnist’, ‘fashionmnist’, ‘cifar’, ‘cifar10’, ‘cifar100’, ‘imagenet’).
input_size : int
Size of images in the dataset (pixels).
net : str or :class:`autogluon.space.Categorical`
Which existing neural network models to consider as candidates.
optimizer : str or :class:`autogluon.space.AutoGluonObject`
Which optimizers to consider as candidates for learning the neural network weights.
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.
final_fit_epochs: int, default None
Final fit epochs, the same number of epochs will be used as during the HPO if not specified.
metric : str or callable object
Evaluation metric by which predictions will be ulitmately evaluated on test data.
loss : `mxnet.gluon.loss`
Loss function used during training of the neural network weights.
num_trials : int
Maximal number of hyperparameter configurations to try out.
time_limits : int
Approximately how long `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).
split_ratio : float, default = 0.8
Fraction of dataset to use for training (rest of data is held-out for tuning hyperparameters).
The final returned model may be fit to all of the data (after hyperparameters have been selected).
nthreads_per_trial : int
How many CPUs to use in each trial (ie. single training run of a model).
ngpus_per_trial : int
How many GPUs to use in each trial (ie. single training run of a model).
output_directory : str
Checkpoints of the search state are written to
os.path.join(output_directory, 'exp1.ag')
scheduler_options : dict
Extra arguments passed to __init__ of scheduler, to configure the
orchestration of training jobs during hyperparameter-tuning.
search_strategy : str, default = None
Which hyperparameter search algorithm to use. Options include:
'random' (random search), 'bayesopt' (Gaussian process Bayesian optimization),
'skopt' (SKopt Bayesian optimization), 'grid' (grid search),
'hyperband' (Hyperband scheduling with random search), 'bayesopt-hyperband'
(Hyperband scheduling with GP-BO search).
If unspecified, the default is 'random'.
search_options : dict
Auxiliary keyword arguments to pass to the searcher that performs
hyperparameter optimization.
resume : bool
If True, the hyperparameter search is started from state loaded from
os.path.join(output_directory, 'exp1.ag')
dist_ip_addrs : list
List of IP addresses corresponding to remote workers, in order to leverage distributed computation.
verbose : bool
Whether or not to print out intermediate information during training.
plot_results : bool
Whether or not to generate plots summarizing training process.
visualizer : str
Describes method to visualize training progress during `fit()`. Options: ['mxboard', 'tensorboard', 'none'].
auto_search : bool
If True, enables automatic suggestion of network types and hyper-parameter ranges adaptively based on provided dataset.
Returns
-------
:class:`autogluon.task.image_classification.Classifier` object which can make predictions on new data and summarize what happened during `fit()`.
Examples
--------
>>> from autogluon.vision import ImageClassification as task
>>> dataset = task.Dataset(train_path='data/train',
>>> test_path='data/test')
>>> classifier = task.fit(dataset,
>>> nets=ag.space.Categorical['resnet18_v1', 'resnet34_v1'],
>>> time_limits=time_limits,
>>> ngpus_per_trial=1,
>>> num_trials = 4)
>>> test_data = task.Dataset('~/data/test', train=False)
>>> test_acc = classifier.evaluate(test_data)
Bag of tricks are used on image classification dataset
lr_config
----------
lr-mode : type=str, default='step'.
describes how learning rate should be adjusted over the course of training. Options include: 'cosine', 'poly'.
lr-decay : type=float, default=0.1.
decay rate of learning rate. default is 0.1.
lr-decay-period : type=int, default=0.
interval for periodic learning rate decays. default is 0 to disable.
lr-decay-epoch : type=str, default='10,20,30'.
epochs at which learning rate decays. epochs=40, default is 10, 20, 30.
warmup-lr : type=float, default=0.0.
starting warmup learning rate. default is 0.0.
warmup-epochs : type=int, default=0.
number of warmup epochs.
tricks
----------
last-gamma', default= True.
whether to init gamma of the last BN layer in each bottleneck to 0.
use-pretrained', default= True.
enable using pretrained model from gluon.
use_se', default= False.
use SE layers or not in resnext. default is false.
mixup', default= False.
whether train the model with mix-up. default is false.
mixup-alpha', type=float, default=0.2.
beta distribution parameter for mixup sampling, default is 0.2.
mixup-off-epoch', type=int, default=0.
how many last epochs to train without mixup, default is 0.
label-smoothing', default= True.
use label smoothing or not in training. default is false.
no-wd', default= True.
whether to remove weight decay on bias, and beta/gamma for batchnorm layers.
teacher', type=str, default=None.
teacher model for distillation training
temperature', type=float, default=20.
temperature parameter for distillation teacher model
hard-weight', type=float, default=0.5.
weight for the loss of one-hot label for distillation training
batch-norm', default= True.
enable batch normalization or not in vgg. default is false.
use-gn', default= False.
whether to use group norm.
"""
checkpoint = os.path.join(output_directory, 'exp1.ag')
if auto_search:
# The strategies can be injected here, for example: automatic suggest some hps
# based on the dataset statistics
net = auto_suggest_network(dataset, net)
nthreads_per_trial = get_cpu_count(
) if nthreads_per_trial > get_cpu_count() else nthreads_per_trial
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
final_fit_epochs = final_fit_epochs if final_fit_epochs else epochs
train_image_classification.register_args(
dataset=dataset,
net=net,
optimizer=optimizer,
loss=loss,
metric=metric,
num_gpus=ngpus_per_trial,
split_ratio=split_ratio,
batch_size=batch_size,
input_size=input_size,
epochs=epochs,
final_fit_epochs=final_fit_epochs,
verbose=verbose,
num_workers=nthreads_per_trial,
hybridize=hybridize,
final_fit=False,
tricks=tricks,
lr_config=lr_config)
# 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=resume,
visualizer=visualizer,
time_attr='epoch',
reward_attr='classification_reward',
dist_ip_addrs=dist_ip_addrs,
epochs=epochs)
results = BaseTask.run_fit(train_image_classification,
search_strategy,
scheduler_options,
plot_results=plot_results)
args = sample_config(train_image_classification.args,
results['best_config'])
kwargs = {'num_classes': results['num_classes'], 'ctx': mx.cpu(0)}
model = get_network(args.net, **kwargs)
multi_precision = optimizer.kwvars[
'multi_precision'] if 'multi_precision' in optimizer.kwvars else False
update_params(model, results.pop('model_params'), multi_precision)
if ensemble > 1:
models = [model]
scheduler = create_scheduler(train_image_classification,
search_strategy, scheduler_options)
for i in range(1, ensemble):
resultsi = scheduler.run_with_config(results['best_config'])
kwargs = {
'num_classes': resultsi['num_classes'],
'ctx': mx.cpu(0)
}
model = get_network(args.net, **kwargs)
update_params(model, resultsi.pop('model_params'),
multi_precision)
models.append(model)
model = Ensemble(models)
results.pop('args')
args.pop('optimizer')
args.pop('dataset')
args.pop('loss')
return Classifier(model, results, default_val_fn, checkpoint, args)
def load_state_dict(self, state_dict):
try_import_mxnet()
from autogluon.mxnet.utils import update_params
self._results = pickle.loads(state_dict['results'])
update_params(self.controller,
pickle.loads(state_dict['controller_params']))