0cc8f2be01456c1e91f160e7fd8f1d830e3332ae,train.py,,main,#Any#,30

Before Change

                rloss[key] = (rloss[key] * ui + val) / (ui + 1)

            // Precision
            precision = metrics[0] / (metrics[0] + metrics[1] + 1e-16)
            k = (metrics[0] + metrics[1]) > 0
            if k.sum() > 0:
                mean_precision = precision[k].mean()

After Change

            // accumulated_batches = 4  // accumulate gradient for 4 batches before stepping optimizer
            // if ((i+1) % accumulated_batches == 0) or (i == len(dataloader) - 1):
            optimizer.step()
            optimizer.zero_grad()

            // Compute running epoch-means of tracked metrics
            ui += 1
            metrics += model.losses["metrics"]

Instances