def forward()

in src/fairseq/fairseq/criterions/binary_cross_entropy.py [0:0]

• 58 lines of code
• 24 McCabe index (conditional complexity)

    def forward(self, model, sample, reduce=True, log_pred=False):
        """Compute the loss for the given sample.

        Returns a tuple with three elements:
        1) the loss
        2) the sample size, which is used as the denominator for the gradient
        3) logging outputs to display while training
        """
        net_output = model(**sample['net_input'])
        logits = model.get_logits(net_output).float()
        target = model.get_targets(sample, net_output)

        weights = None
        if hasattr(model, 'get_target_weights') and not self.infonce:
            weights = model.get_target_weights(target, net_output)
            if torch.is_tensor(weights):
                weights = weights.float()

        losses = []

        if self.infonce:
            loss = F.cross_entropy(logits, target, reduction="sum" if reduce else "none",)
        else:
            loss = F.binary_cross_entropy_with_logits(logits, target.float(), weights, reduction="sum" if reduce else "none",)

        sample_size = target.numel() if self.infonce else target.long().sum().item()
        losses.append(loss)

        if self.loss_weights is not None and hasattr(model, "get_extra_losses"):
            extra_losses = model.get_extra_losses(net_output)
            if torch.is_tensor(extra_losses):
                extra_losses = [extra_losses]
            if len(self.loss_weights) == 1 and len(extra_losses) != 1:
                self.loss_weights = [self.loss_weights[0]] * len(extra_losses)
            assert len(extra_losses) == len(self.loss_weights), f'{len(extra_losses)}, {len(self.loss_weights)}'
            for p, coef in zip(extra_losses, self.loss_weights):
                if coef != 0 and p is not None:
                    p = coef * p.float() * sample_size
                    loss += p
                    losses.append(p)

        logging_output = {
            'loss': loss.item() if reduce else loss,
            'ntokens': sample_size,
            'nsentences': logits.size(0),
            'sample_size': sample_size,
        }

        for lk in self.log_keys:
            if lk in net_output:
                logging_output[lk] = float((net_output[lk]))

        if len(losses) > 1:
            for i, l in enumerate(losses):
                logging_output[f'loss_{i}'] = l.item()

        if self.infonce:
            with torch.no_grad():
                if logits.numel() == 0:
                    corr = 0
                    count = 0
                else:
                    assert logits.dim() > 1, logits.shape
                    max = logits.argmax(-1) == 0
                    min = logits.argmin(-1) == 0
                    both = max & min
                    corr = max.long().sum().item() - both.long().sum().item()
                    count = max.numel()

                logging_output["correct"] = corr
                logging_output["count"] = count

        if log_pred:
            logging_output['logits'] = logits.cpu().numpy()
            logging_output['target'] = target.cpu().numpy()
        return loss, sample_size, logging_output