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import torch
from torchmetrics import Metric
class MyAccuracy(Metric):
"""
Accuracy metric costomized for handling sequences with padding.
Methods:
update(self, logits, labels, num_labels): Update the accuracy based on
model predictions and ground truth labels.
compute(self): Compute the accuracy.
Attributes:
total (torch.Tensor): Total number of non-padding elements.
correct (torch.Tensor): Number of correctly predicted non-padding elements.
"""
def __init__(self):
super().__init__()
self.add_state('total', default=torch.tensor(0), dist_reduce_fx='sum')
self.add_state('correct', default=torch.tensor(0), dist_reduce_fx='sum')
def update(self, logits: torch.Tensor, labels: torch.Tensor, num_labels: int) -> None:
"""
Args:
logits (torch.Tensor): Model predictions.
labels (torch.Tensor): Ground truth labels.
num_labels (int): Number of unique labels.
"""
flattened_targets = labels.view(-1) # shape (batch_size, sequence_len)
active_logits = logits.view(-1, num_labels) # shape (batch_size * sequence_len, num_labels)
flattened_predictions = torch.argmax(active_logits, axis=1) # shape (batch_size * sequence_len)
# compute accuracy only at active labels
active_accuracy = labels.view(-1) != -100 # shape (batch_size, sequnce_len)
ac_labels = torch.masked_select(flattened_targets, active_accuracy)
predictions = torch.masked_select(flattened_predictions, active_accuracy)
self.correct += torch.sum(ac_labels == predictions)
self.total += torch.numel(ac_labels)
def compute(self) -> torch.Tensor:
"""
Calculate the accuracy.
"""
return self.correct.float() / self.total.float() |