def forward()

in utils.py [0:0]

• 26 lines of code
• 7 McCabe index (conditional complexity)

    def forward(ctx, log_probs, targets, blank_idx=0, reduction="none"):
        B, T, C = log_probs.shape
        losses = [None] * B
        scales = [None] * B
        emissions_graphs = [None] * B

        def process(b):
            # create emission graph
            g_emissions = gtn.linear_graph(T, C, log_probs.requires_grad)
            cpu_data = log_probs[b].cpu().contiguous()
            g_emissions.set_weights(cpu_data.data_ptr())

            # create criterion graph
            g_criterion = CTCLossFunction.create_ctc_graph(targets[b], blank_idx)
            # compose the graphs
            g_loss = gtn.negate(
                gtn.forward_score(gtn.intersect(g_emissions, g_criterion))
            )

            scale = 1.0
            if reduction == "mean":
                L = len(targets[b])
                scale = 1.0 / L if L > 0 else scale
            elif reduction != "none":
                raise ValueError("invalid value for reduction '" + str(reduction) + "'")

            # Save for backward:
            losses[b] = g_loss
            scales[b] = scale
            emissions_graphs[b] = g_emissions

        gtn.parallel_for(process, range(B))

        ctx.auxiliary_data = (losses, scales, emissions_graphs, log_probs.shape)
        loss = torch.tensor([losses[b].item() * scales[b] for b in range(B)])
        return torch.mean(loss.cuda() if log_probs.is_cuda else loss)