79928c84cdf990ef6fe1043a3e4f74b9cc252642,eval.py,,eval_net,#Any#Any#Any#Any#,8

Before Change

    net.eval()
    tot = 0

    for i, b in tqdm(enumerate(loader), desc="Validation round", unit="img"):
        imgs = b["image"]
        true_masks = b["mask"]

After Change

                    tot += F.cross_entropy(mask_pred.unsqueeze(dim=0), true_mask.unsqueeze(dim=0)).item()
                else:
                    tot += dice_coeff(mask_pred, true_mask.squeeze(dim=1)).item()
            pbar.update(imgs.shape[0])

    return tot / n_val

Instances