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"""
Model Factory Functions
"""
from .architectures.hybrid import HybridLatentODEModel
from .architectures.vanilla_ode import VanillaODEModel
from .architectures.event_ode import EventODEModel
from .architectures.augmented_ode import AugmentedODEModel
from .architectures.autoregressive import AutoregressiveModel
def create_model(model_type, config, device):
"""
Factory function to create different types of models
Args:
model_type: 'hybrid', 'vanilla_ode', 'event_ode', 'augmented_ode', 'autoregressive'
config: Configuration dictionary
device: PyTorch device
Returns:
model: Instance of BaseModel subclass
"""
if model_type == 'hybrid':
from .architectures.hybrid import HybridLatentODEModel
return HybridLatentODEModel(config, device)
elif model_type == 'vanilla_ode':
from .architectures.vanilla_ode import VanillaODEModel
return VanillaODEModel(config, device)
elif model_type == 'event_ode':
from .architectures.event_ode import EventODEModel
return EventODEModel(config, device)
elif model_type == 'augmented_ode':
from .architectures.augmented_ode import AugmentedODEModel
return AugmentedODEModel(config, device)
elif model_type == 'autoregressive':
from .architectures.autoregressive import AutoregressiveModel
return AutoregressiveModel(config, device)
else:
raise ValueError(f"Unknown model type: {model_type}")
# Deprecated functions for backward compatibility
def create_hybrid_models(config, device):
"""Deprecated: Use create_model('hybrid', config, device) instead"""
model = HybridLatentODEModel(config, device)
models = {
'encoder': model.encoder,
'vector_field': model.vector_field,
'decoder': model.decoder
}
optimizers = model.optimizers
return models, optimizers
def create_neural_ode_models(config, device):
"""Deprecated: Use create_model('vanilla_ode', config, device) instead"""
model = VanillaODEModel(config, device)
models = {'vector_field': model.vector_field}
optimizers = {'optimizer': model.optimizers['vector_field']}
return models, optimizers
def create_event_ode_models(config, device):
"""Deprecated: Use create_model('event_ode', config, device) instead"""
model = EventODEModel(config, device)
models = {
'vector_field': model.vector_field,
'event_function': model.event_function,
'state_reset': model.state_reset
}
optimizers = {'optimizer': model.optimizers['combined']}
return models, optimizers |