def get_default_pipeline(cls):
from autoPyTorch.pipeline.base.pipeline import Pipeline
from autoPyTorch.pipeline.nodes.image.optimization_algorithm_no_timelimit import OptimizationAlgorithmNoTimeLimit
from autoPyTorch.pipeline.nodes.one_hot_encoding import OneHotEncoding
from autoPyTorch.pipeline.nodes.optimizer_selector import OptimizerSelector
from autoPyTorch.pipeline.nodes.log_functions_selector import LogFunctionsSelector
from autoPyTorch.pipeline.nodes.metric_selector import MetricSelector
from autoPyTorch.pipeline.nodes.image.simple_scheduler_selector import SimpleLearningrateSchedulerSelector
from autoPyTorch.pipeline.nodes.image.cross_validation_indices import CrossValidationIndices
from autoPyTorch.pipeline.nodes.image.autonet_settings_no_shuffle import AutoNetSettingsNoShuffle
from autoPyTorch.pipeline.nodes.image.network_selector_datasetinfo import NetworkSelectorDatasetInfo
from autoPyTorch.pipeline.nodes.image.loss_module_selector_indices import LossModuleSelectorIndices
from autoPyTorch.pipeline.nodes.image.image_augmentation import ImageAugmentation
from autoPyTorch.pipeline.nodes.image.create_image_dataloader import CreateImageDataLoader
from autoPyTorch.pipeline.nodes.image.create_dataset_info import CreateDatasetInfo
from autoPyTorch.pipeline.nodes.image.simple_train_node import SimpleTrainNode
from autoPyTorch.pipeline.nodes.image.image_dataset_reader import ImageDatasetReader
from autoPyTorch.pipeline.nodes.image.single_dataset import SingleDataset
# build the pipeline
pipeline = Pipeline([
AutoNetSettingsNoShuffle(),
OptimizationAlgorithmNoTimeLimit([
SingleDataset([
ImageDatasetReader(),
CreateDatasetInfo(),
CrossValidationIndices([
NetworkSelectorDatasetInfo(),
OptimizerSelector(),
SimpleLearningrateSchedulerSelector(),
LogFunctionsSelector(),
MetricSelector(),
LossModuleSelectorIndices(),
ImageAugmentation(),
CreateImageDataLoader(),
SimpleTrainNode()
])
])
])
])
cls._apply_default_pipeline_settings(pipeline)
return pipeline
def _apply_default_pipeline_settings(pipeline):
from autoPyTorch.pipeline.nodes.optimizer_selector import OptimizerSelector
from autoPyTorch.pipeline.nodes.image.simple_scheduler_selector import SimpleLearningrateSchedulerSelector
from autoPyTorch.pipeline.nodes.image.network_selector_datasetinfo import NetworkSelectorDatasetInfo
from autoPyTorch.pipeline.nodes.image.simple_train_node import SimpleTrainNode
from autoPyTorch.pipeline.nodes.image.create_image_dataloader import CreateImageDataLoader
from autoPyTorch.pipeline.nodes.image.image_augmentation import ImageAugmentation
from autoPyTorch.components.networks.image import DenseNet, ResNet, MobileNet
from autoPyTorch.components.networks.image.densenet_flexible import DenseNetFlexible
from autoPyTorch.components.networks.image.resnet152 import ResNet152
from autoPyTorch.components.networks.image.darts.model import DARTSImageNet
from autoPyTorch.components.optimizer.optimizer import AdamOptimizer, AdamWOptimizer, SgdOptimizer, RMSpropOptimizer
from autoPyTorch.components.lr_scheduler.lr_schedulers import SchedulerCosineAnnealingWithRestartsLR, SchedulerNone, \
SchedulerCyclicLR, SchedulerExponentialLR, SchedulerReduceLROnPlateau, SchedulerReduceLROnPlateau, SchedulerStepLR, SchedulerAlternatingCosineLR, SchedulerAdaptiveLR, SchedulerExponentialLR
from autoPyTorch.components.training.image.early_stopping import EarlyStopping
from autoPyTorch.components.training.image.mixup import Mixup
net_selector = pipeline[NetworkSelectorDatasetInfo.get_name()]
net_selector.add_network('densenet', DenseNet)
net_selector.add_network('densenet_flexible', DenseNetFlexible)
net_selector.add_network('resnet', ResNet)
net_selector.add_network('resnet152', ResNet152)
net_selector.add_network('darts', DARTSImageNet)
net_selector.add_network('mobilenet', MobileNet)
net_selector._apply_search_space_update('resnet:nr_main_blocks',
[2, 4],
log=False)
net_selector._apply_search_space_update('resnet:widen_factor_1',
[0.5, 8],
log=True)
opt_selector = pipeline[OptimizerSelector.get_name()]
opt_selector.add_optimizer('adam', AdamOptimizer)
opt_selector.add_optimizer('adamw', AdamWOptimizer)
opt_selector.add_optimizer('sgd', SgdOptimizer)
opt_selector.add_optimizer('rmsprop', RMSpropOptimizer)
lr_selector = pipeline[SimpleLearningrateSchedulerSelector.get_name()]
lr_selector.add_lr_scheduler('cosine_annealing',
SchedulerCosineAnnealingWithRestartsLR)
lr_selector.add_lr_scheduler('cyclic', SchedulerCyclicLR)
lr_selector.add_lr_scheduler('step', SchedulerStepLR)
lr_selector.add_lr_scheduler('adapt', SchedulerAdaptiveLR)
lr_selector.add_lr_scheduler('plateau', SchedulerReduceLROnPlateau)
lr_selector.add_lr_scheduler('alternating_cosine',
SchedulerAlternatingCosineLR)
lr_selector.add_lr_scheduler('exponential', SchedulerExponentialLR)
lr_selector.add_lr_scheduler('none', SchedulerNone)
lr_selector._apply_search_space_update('step:step_size', [1, 100],
log=True)
lr_selector._apply_search_space_update('step:gamma', [0.001, 0.99],
log=True)
lr_selector._apply_search_space_update('cosine_annealing:T_max',
[1, 100],
log=True)
lr_selector._apply_search_space_update('cosine_annealing:T_mult',
[1., 2.],
log=False)
train_node = pipeline[SimpleTrainNode.get_name()]
#train_node.add_training_technique("early_stopping", EarlyStopping)
train_node.add_batch_loss_computation_technique("mixup", Mixup)
data_node = pipeline[CreateImageDataLoader.get_name()]
data_node._apply_search_space_update('batch_size', [32, 160], log=True)
augment_node = pipeline[ImageAugmentation.get_name()]
augment_node._apply_search_space_update('augment', [False, True])
augment_node._apply_search_space_update('autoaugment', [False, True])
augment_node._apply_search_space_update('fastautoaugment',
[False, True])
augment_node._apply_search_space_update('length', [2, 6])
augment_node._apply_search_space_update('cutout', [False, True])
augment_node._apply_search_space_update('cutout_holes', [1, 50])