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import torch.nn as nn
from .abstract_loss_func import AbstractLossClass
from metrics.registry import LOSSFUNC
@LOSSFUNC.register_module(module_name="bce")
class BCELoss(AbstractLossClass):
def __init__(self):
super().__init__()
self.loss_fn = nn.BCELoss()
def forward(self, inputs, targets):
"""
Computes the bce loss.
Args:
inputs: A PyTorch tensor of size (batch_size, num_classes) containing the predicted scores.
targets: A PyTorch tensor of size (batch_size) containing the ground-truth class indices.
Returns:
A scalar tensor representing the bce loss.
"""
# Compute the bce loss
loss = self.loss_fn(inputs, targets.float())
return loss |