def configs() -> Configs: c = Configs() c.add( name="target_gflops", type=float, default=2.0, strategy="constant", description="Target (Giga) Floating Point Operations per Second.", ) return c
def configs(): c = Configs() c.add( name="hidden_dim", type=int, default=128, strategy="choice", choices=[128, 256, 512, 1024], description="Number of hiden units.", ) return c
def configs() -> Configs: c = Configs() c.add( name="extract_features_after_layer", default="", type=str, description=( "Layer name after which to extract features. " "Nested layers may be selected using dot-notation, " "e.g. `block.subblock.layer1`" ), ) return c
def configs() -> Configs: c = Configs() c.add( name="learning_rate", type=float, default=0.001, choices=(5e-7, 5e-1), strategy="loguniform", description="Learning rate.", ) c.add( name="optimizer_beta1", type=float, default=0.9, choices=(0, 0.999), strategy="uniform", description="Beta 1.", ) c.add( name="optimizer_beta2", type=float, default=0.999, choices=(0, 0.99999), strategy="uniform", description="Beta 2.", ) c.add( name="weight_decay", type=float, default=1e-2, choices=(1e-6, 1e-1), strategy="loguniform", description="Weight decay.", ) return c
def configs() -> Configs: c = Configs() c.add( name="metric_selection", default=default_config, type=str, strategy="constant", description="Selection key for MetricSelector.", choices=list(mapping.keys()), ) for Metric in metric_set: if hasattr(Metric, "configs"): c += Metric.configs() return c
def configs() -> Configs: c = Configs() c.add( name="optimization_metric", default="loss", type=str, choices=MetricMixin.metric_names(), description= "Name of the performance metric that should be optimized", ) c.add( name="test_ensemble", type=int, default=0, strategy="constant", description= "Flag indicating whether the test dataset should yield a clip ensemble.", ) c.add( name="gpus", type=str, default=None, strategy="constant", description= "Which gpus should be used. Can be either the number of gpus (e.g. '2') or a list of gpus (e.g. ('0,1').", ) c.add( name="loss", type=str, default="cross_entropy", choices=loss_names, strategy="constant", description="Loss function used during optimisation.", ) return c
def configs(self) -> Configs: c = Configs() c.add( name="trials", default=30, type=int, description="Number of trials in the hyperparameter search", ) c.add( name="gpus_per_trial", default=0, type=int, description="Number of GPUs per trail in the hyperparameter search", ) c.add( name="optimization_metric", default="loss", type=str, choices=self.Module.metric_names(), description="Name of the performance metric that should be optimized", ) c.add( name="from_hparam_space_file", default=None, type=str, description="Path to file with specification for the search space during hyper-parameter optimisation", ) return c
def configs() -> Configs: c = Configs() c.add( name="learning_rate", type=float, default=0.1, choices=(5e-2, 5e-1), strategy="loguniform", description="Learning rate.", ) c.add( name="weight_decay", type=float, default=1e-5, choices=(1e-6, 1e-3), strategy="loguniform", description="Weight decay.", ) c.add( name="momentum", type=float, default=0.9, choices=(0, 0.999), strategy="uniform", description="Momentum.", ) return c
def configs() -> Configs: c = Configs() c.add( name="unfreeze_from_epoch", type=int, default=-1, description= "Number of epochs to wait before starting gradual unfreeze. If -1, unfreeze is omitted.", ) c.add( name="unfreeze_layers_must_include", type=str, default="", description= "String that must be contained in layer names which should be unfrozen. If empty, this feature is disabled.", ) c.add( name="unfreeze_epoch_step", type=int, default=1, description="Number of epochs to train before next unfreeze.", ) c.add( name="unfreeze_layers_initial", type=int, default=1, strategy="choice", description= "Number layers to unfreeze initially. If `-1`, it will be equal to total_layers", ) c.add( name="unfreeze_layer_step", type=int, default=1, description= "Number additional layers to unfreeze at each unfreeze step. If `-1`, all layers are unfrozon after a step", ) c.add( name="unfreeze_layers_max", type=int, default=-1, description= "Maximum number of layers to unfreeze. If `-1`, it will be equal to total_layers", ) return c