direct.config package#
Submodules#
direct.config.defaults module#
- class direct.config.defaults.TensorboardConfig(num_images=8)[source]#
Bases:
BaseConfig- num_images = 8#
- __init__(num_images=8)#
- class direct.config.defaults.LoggingConfig(log_as_image=None, tensorboard=<factory>)[source]#
Bases:
BaseConfig- log_as_image = None#
- tensorboard#
- __init__(log_as_image=None, tensorboard=<factory>)#
- class direct.config.defaults.FunctionConfig(function='???', multiplier=1.0)[source]#
Bases:
BaseConfig- function = '???'#
- multiplier = 1.0#
- __init__(function='???', multiplier=1.0)#
- class direct.config.defaults.CheckpointerConfig(checkpoint_steps=500)[source]#
Bases:
BaseConfig- checkpoint_steps = 500#
- __init__(checkpoint_steps=500)#
- class direct.config.defaults.LossConfig(crop=None, losses=<factory>)[source]#
Bases:
BaseConfig- crop = None#
- losses#
- __init__(crop=None, losses=<factory>)#
- class direct.config.defaults.TrainingConfig(datasets=<factory>, model_checkpoint=None, optimizer='Adam', lr=0.0005, weight_decay=1e-06, batch_size=2, lr_step_size=5000, lr_gamma=0.5, lr_warmup_iter=500, swa_start_iter=None, num_iterations=50000, validation_steps=1000, gradient_steps=1, gradient_clipping=0.0, gradient_debug=False, loss=<factory>, checkpointer=<factory>, metrics=<factory>, regularizers=<factory>)[source]#
Bases:
BaseConfig- datasets#
- model_checkpoint = None#
- optimizer = 'Adam'#
- lr = 0.0005#
- weight_decay = 1e-06#
- batch_size = 2#
- lr_step_size = 5000#
- lr_gamma = 0.5#
- lr_warmup_iter = 500#
- swa_start_iter = None#
- num_iterations = 50000#
- validation_steps = 1000#
- gradient_steps = 1#
- gradient_clipping = 0.0#
- gradient_debug = False#
- loss#
- checkpointer#
- metrics#
- regularizers#
- __init__(datasets=<factory>, model_checkpoint=None, optimizer='Adam', lr=0.0005, weight_decay=1e-06, batch_size=2, lr_step_size=5000, lr_gamma=0.5, lr_warmup_iter=500, swa_start_iter=None, num_iterations=50000, validation_steps=1000, gradient_steps=1, gradient_clipping=0.0, gradient_debug=False, loss=<factory>, checkpointer=<factory>, metrics=<factory>, regularizers=<factory>)#
- class direct.config.defaults.ValidationConfig(datasets=<factory>, batch_size=8, metrics=<factory>, regularizers=<factory>, crop='training')[source]#
Bases:
BaseConfig- datasets#
- batch_size = 8#
- metrics#
- regularizers#
- crop = 'training'#
- __init__(datasets=<factory>, batch_size=8, metrics=<factory>, regularizers=<factory>, crop='training')#
- class direct.config.defaults.InferenceConfig(dataset=<factory>, batch_size=1, crop=None)[source]#
Bases:
BaseConfig- dataset#
- batch_size = 1#
- crop = None#
- __init__(dataset=<factory>, batch_size=1, crop=None)#
- class direct.config.defaults.ModelConfig(model_name='???', engine_name=None)[source]#
Bases:
BaseConfig- model_name = '???'#
- engine_name = None#
- __init__(model_name='???', engine_name=None)#
- class direct.config.defaults.PhysicsConfig(forward_operator='fft2', backward_operator='ifft2', use_noise_matrix=False, noise_matrix_scaling=1.0)[source]#
Bases:
BaseConfig- forward_operator = 'fft2'#
- backward_operator = 'ifft2'#
- use_noise_matrix = False#
- noise_matrix_scaling = 1.0#
- __init__(forward_operator='fft2', backward_operator='ifft2', use_noise_matrix=False, noise_matrix_scaling=1.0)#
- class direct.config.defaults.DefaultConfig(model='???', additional_models=None, physics=<factory>, training=<factory>, validation=<factory>, inference=None, logging=<factory>)[source]#
Bases:
BaseConfig- model = '???'#
- additional_models = None#
- physics#
- training#
- validation#
- inference = None#
- logging#
- __init__(model='???', additional_models=None, physics=<factory>, training=<factory>, validation=<factory>, inference=None, logging=<factory>)#