def run_vgg_flower():
vgg_flower_database = VGGFlowerDatabase()
maml = ModelAgnosticMetaLearningModel(
database=vgg_flower_database,
network_cls=MiniImagenetModel,
n=5,
k_ml=1,
k_val_ml=5,
k_val=1,
k_val_val=15,
k_test=1,
k_val_test=15,
meta_batch_size=4,
num_steps_ml=5,
lr_inner_ml=0.05,
num_steps_validation=5,
save_after_iterations=15000,
meta_learning_rate=0.001,
report_validation_frequency=1000,
log_train_images_after_iteration=1000,
num_tasks_val=100,
clip_gradients=True,
experiment_name='dtd',
val_seed=42,
val_test_batch_norm_momentum=0.0,
)
maml.train(iterations=60040)
maml.evaluate(50, num_tasks=1000, seed=42, use_val_batch_statistics=True)
maml.evaluate(50, num_tasks=1000, seed=42, use_val_batch_statistics=False)
def run_domain_attention():
train_domain_databases = [
MiniImagenetDatabase(),
OmniglotDatabase(random_seed=47,
num_train_classes=1200,
num_val_classes=100),
DTDDatabase(),
VGGFlowerDatabase()
]
meta_train_domain_databases = [
# AirplaneDatabase(),
FungiDatabase(),
# CUBDatabase(),
]
test_database = FungiDatabase()
da = DomainAttentionUnsupervised(
train_databases=train_domain_databases,
meta_train_databases=meta_train_domain_databases,
database=test_database,
test_database=test_database,
network_cls=None,
image_shape=(84, 84, 3),
n=5,
k_ml=1,
k_val_ml=5,
k_val=1,
k_val_val=15,
k_test=5,
k_val_test=15,
meta_batch_size=4,
num_steps_ml=1,
lr_inner_ml=0.05,
num_steps_validation=5,
save_after_iterations=5000,
meta_learning_rate=0.001,
report_validation_frequency=1000,
log_train_images_after_iteration=1000,
num_tasks_val=100,
clip_gradients=True,
experiment_name=
'domain_attention_all_frozen_layers_unsupervised_fungi2',
val_seed=42,
val_test_batch_norm_momentum=0.0,
)
print(da.experiment_name)
# da.train(iterations=5000)
da.evaluate(iterations=50, num_tasks=1000, seed=42)
def __init__(self, meta_train_databases=None, *args, **kwargs):
super(CombinedCrossDomainMetaLearning, self).__init__(*args, **kwargs)
if meta_train_databases is None:
self.meta_train_databases = [
MiniImagenetDatabase(),
AirplaneDatabase(),
CUBDatabase(),
OmniglotDatabase(random_seed=47,
num_train_classes=1200,
num_val_classes=100),
DTDDatabase(),
FungiDatabase(),
VGGFlowerDatabase()
]
else:
self.meta_train_databases = meta_train_databases
def run_domain_attention():
train_domain_databases = [
MiniImagenetDatabase(),
OmniglotDatabase(random_seed=47,
num_train_classes=1200,
num_val_classes=100),
DTDDatabase(),
VGGFlowerDatabase()
]
meta_train_domain_databases = [
AirplaneDatabase(),
FungiDatabase(),
CUBDatabase(),
]
test_database = EuroSatDatabase()
ewda = ElementWiseDomainAttention(
train_databases=train_domain_databases,
meta_train_databases=meta_train_domain_databases,
database=test_database,
test_database=test_database,
network_cls=None,
image_shape=(84, 84, 3),
n=5,
k_ml=1,
k_val_ml=5,
k_val=1,
k_val_val=15,
k_test=5,
k_val_test=15,
meta_batch_size=4,
num_steps_ml=5,
lr_inner_ml=0.05,
num_steps_validation=5,
save_after_iterations=15000,
meta_learning_rate=0.001,
report_validation_frequency=1000,
log_train_images_after_iteration=1000,
num_tasks_val=100,
clip_gradients=True,
experiment_name='element_wise_domain_attention',
val_seed=42,
val_test_batch_norm_momentum=0.0,
)
ewda.train(iterations=60000)
ewda.evaluate(iterations=50, num_tasks=1000, seed=14)
def get_train_dataset(self):
databases = [
MiniImagenetDatabase(),
AirplaneDatabase(),
CUBDatabase(),
OmniglotDatabase(random_seed=47, num_train_classes=1200, num_val_classes=100),
DTDDatabase(),
FungiDatabase(),
VGGFlowerDatabase()
]
dataset = self.get_cross_domain_meta_learning_dataset(
databases=databases,
n=self.n,
k_ml=self.k_ml,
k_validation=self.k_val_ml,
meta_batch_size=self.meta_batch_size
)
return dataset
def run_airplane():
test_database = VGGFlowerDatabase()
cdae = CrossDomainAE2(
database=test_database,
batch_size=512,
# domains=('fungi', ),
# domains=('airplane', 'fungi', 'cub', 'dtd', 'miniimagenet', 'omniglot', 'vggflowers'),
domains=('airplane', 'fungi', 'cub', 'dtd', 'miniimagenet',
'omniglot'),
# domains=('cub', 'miniimagenet', 'vggflowers'),
# domains=('fungi', 'cub', 'dtd', 'miniimagenet', 'omniglot', 'vggflowers'),
)
experiment_name = 'all_domains_288'
cdae.train(epochs=20, experiment_name=experiment_name)
cdae.evaluate(10,
num_tasks=1000,
k_test=1,
k_val_test=15,
inner_learning_rate=0.001,
experiment_name=experiment_name,
seed=42)
if __name__ == '__main__':
tf.config.set_visible_devices([], 'GPU')
root_folder_to_save = os.path.expanduser('~/datasets_visualization/')
if not os.path.exists(root_folder_to_save):
os.mkdir(root_folder_to_save)
databases = (
MiniImagenetDatabase(),
OmniglotDatabase(random_seed=42,
num_train_classes=1200,
num_val_classes=100),
AirplaneDatabase(),
CUBDatabase(),
DTDDatabase(),
FungiDatabase(),
VGGFlowerDatabase(),
# TrafficSignDatabase(),
# MSCOCODatabase(),
# PlantDiseaseDatabase(),
# EuroSatDatabase(),
# ISICDatabase(),
# ChestXRay8Database(),
)
# for database in databases:
# visualize_database(database, root_folder_to_save)
visualize_all_domains_together(databases, root_folder_to_save)
from databases import VGGFlowerDatabase
from models.maml_umtra.maml_umtra import MAMLUMTRA
from networks.maml_umtra_networks import MiniImagenetModel
if __name__ == '__main__':
# import tensorflow as tf
# tf.config.experimental_run_functions_eagerly(True)
vgg_flower_database = VGGFlowerDatabase()
maml_umtra = MAMLUMTRA(
database=vgg_flower_database,
network_cls=MiniImagenetModel,
n=5,
k_ml=1,
k_val_ml=1,
k_val=1,
k_val_val=15,
k_test=5,
k_val_test=15,
meta_batch_size=4,
num_steps_ml=5,
lr_inner_ml=0.05,
num_steps_validation=5,
save_after_iterations=15000,
meta_learning_rate=0.001,
report_validation_frequency=250,
log_train_images_after_iteration=1000,
num_tasks_val=100,
clip_gradients=True,
experiment_name='vgg_flower',