八、将标签尺寸label_shape（8,256,256），调整成预测尺寸pred_shape(8,2,256,256)

下面代码仅适用于二分类算法

import torch


def _expand_onehot_labels(labels, label_weights, target_shape, ignore_index):
    """Expand onehot labels to match the size of prediction."""
    bin_labels = labels.new_zeros(target_shape)
    valid_mask = (labels >= 0) & (labels != ignore_index)
    inds = torch.nonzero(valid_mask, as_tuple=True)

    if inds[0].numel() > 0:
        if labels.dim() == 3:
            bin_labels[inds[0], labels[valid_mask], inds[1], inds[2]] = 1
        else:
            bin_labels[inds[0], labels[valid_mask]] = 1

    valid_mask = valid_mask.unsqueeze(1).expand(target_shape).float()

    if label_weights is None:
        bin_label_weights = valid_mask
    else:
        bin_label_weights = label_weights.unsqueeze(1).expand(target_shape)
        bin_label_weights = bin_label_weights * valid_mask

    return bin_labels, bin_label_weights, valid_mask


def transfer_label_2_pred(pred, label, ignore_index=255, weight=None):
    if pred.size(1) == 1:
        # For binary class segmentation, the shape of pred is
        # [N, 2, H, W] and that of label is [N, H, W].
        # As the ignore_index often set as 255, so the
        # binary class label check should mask out
        # ignore_index
        assert label[label != ignore_index].max() <= 1, \
            'For pred with shape [N, 1, H, W], its label must have at ' \
            'most 2 classes'
        pred = pred.squeeze(1)
    if pred.dim() != label.dim():
        assert (pred.dim() == 2 and label.dim() == 1) or (
                pred.dim() == 4 and label.dim() == 3), \
            'Only pred shape [N, C], label shape [N] or pred shape [N, C, ' \
            'H, W], label shape [N, H, W] are supported'
        # `weight` returned from `_expand_onehot_labels`
        # has been treated for valid (non-ignore) pixels
        label, weight, valid_mask = _expand_onehot_labels(
            label, weight, pred.shape, ignore_index)
        return label, weight, valid_mask
    else:
        return label