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Update losses.py #86

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8 changes: 7 additions & 1 deletion losses.py
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Expand Up @@ -89,7 +89,13 @@ def forward(self, features, labels=None, mask=None):
log_prob = logits - torch.log(exp_logits.sum(1, keepdim=True))

# compute mean of log-likelihood over positive
mean_log_prob_pos = (mask * log_prob).sum(1) / mask.sum(1)
# avoid nan loss when there's one sample for a certain class, e.g., 0,1,...1 for bin-cls , this produce nan for 1st in Batch
# which also results in batch total loss as nan. such row should be dropped
pos_per_sample=mask.sum(1) #B
pos_per_sample[pos_per_sample<1e-6]=1.0
mean_log_prob_pos = (mask * log_prob).sum(1) / pos_per_sample #mask.sum(1)

#mean_log_prob_pos = (mask * log_prob).sum(1) / mask.sum(1)

# loss
loss = - (self.temperature / self.base_temperature) * mean_log_prob_pos
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