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Add focal loss option for classification #9

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Add focal loss option for classification #9

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35 changes: 29 additions & 6 deletions spanet/network/jet_reconstruction/jet_reconstruction_training.py
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Original file line number Diff line number Diff line change
Expand Up @@ -186,12 +186,35 @@ def add_classification_loss(
current_target = targets[key]

weight = None if not self.balance_classifications else self.classification_weights[key]
current_loss = F.cross_entropy(
current_prediction,
current_target,
ignore_index=-1,
weight=weight
)
if self.options.classification_focal_gamma == 0:
current_loss = F.cross_entropy(
current_prediction,
current_target,
ignore_index=-1,
weight=weight
)
else:
# From https://github.com/AdeelH/pytorch-multi-class-focal-loss/blob/master/focal_loss.py
log_p = F.log_softmax(current_prediction, dim=1)
ce = F.nll_loss(
log_p,
current_target,
ignore_index=-1,
weight=weight,
reduction='none'
)
# Get true class column from each row
all_rows = torch.arange(len(current_target))
log_pt = log_p[all_rows, current_target]
# Compute focal term: (1 - pt)^gamma
focal_term = (1 - log_pt.exp()) ** self.options.classification_focal_gamma
# Full loss: -alpha * ((1 - pt)^gamma) * log(pt)
if weight is None:
# Take mean
current_loss = torch.mean(focal_term * ce)
else:
# Divide by sum of class weights
current_loss = torch.sum(focal_term * ce) / weight[current_target].sum()

classification_terms.append(self.options.classification_loss_scale * current_loss)

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3 changes: 3 additions & 0 deletions spanet/options.py
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Original file line number Diff line number Diff line change
Expand Up @@ -235,6 +235,9 @@ def __init__(self, event_info_file: str = "", training_file: str = "", validatio
# Scalar term for classification Cross Entropy loss term
self.classification_loss_scale: float = 0.0

# Gamma exponent for classification focal loss. Setting it to 0.0 will disable focal loss and use regular cross-entropy.
self.classification_focal_gamma: float = 0.0

# Automatically balance loss terms using Jacobians.
self.balance_losses: bool = True

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