def __init__(self, config: ScalarModelBase, *args: Any,
**kwargs: Any) -> None:
super().__init__(config, *args, **kwargs)
self.model = config.create_model()
raw_loss = model_util.create_scalar_loss_function(config)
if isinstance(config, SequenceModelBase):
self.loss_fn = lambda model_output, loss: apply_sequence_model_loss(
raw_loss, model_output, loss)
self.target_indices = config.get_target_indices()
self.target_names = [
SequenceMetricsDict.get_hue_name_from_target_index(p)
for p in config.sequence_target_positions
]
else:
self.loss_fn = raw_loss
self.target_indices = []
self.target_names = config.class_names
self.is_classification_model = config.is_classification_model
self.use_mean_teacher_model = config.compute_mean_teacher_model
self.is_binary_classification_or_regression = True if len(
config.class_names) == 1 else False
self.logits_to_posterior_fn = config.get_post_loss_logits_normalization_function(
)
self.loss_type = config.loss_type
# These two fields store the PyTorch Lightning Metrics objects that will compute metrics on validation
# and training set, in particular ones that are not possible to compute from a single minibatch (AUC and alike)
self.train_metric_computers = self.create_metric_computers()
self.val_metric_computers = self.create_metric_computers()
def __init__(self, config: ScalarModelBase, *args: Any,
**kwargs: Any) -> None:
super().__init__(config, *args, **kwargs)
self.model = config.create_model()
raw_loss = model_util.create_scalar_loss_function(config)
self.loss_fn = raw_loss
self.target_names = config.target_names
self.is_classification_model = config.is_classification_model
self.use_mean_teacher_model = config.compute_mean_teacher_model
self.is_binary_classification_or_regression = True if len(
config.class_names) == 1 else False
self.logits_to_posterior_fn = config.get_post_loss_logits_normalization_function(
)
self.loss_type = config.loss_type
# These two fields store the PyTorch Lightning Metrics objects that will compute metrics on validation
# and training set, in particular ones that are not possible to compute from a single minibatch (AUC and alike)
self.train_metric_computers = config.create_metric_computers()
self.val_metric_computers = config.create_metric_computers()
self.compute_and_log_metrics = config.compute_and_log_metrics
