目标：让预测深度 depth 的相对前后顺序尽量与先验深度 prior_depth 一致（不要求先验尺度准确）。
像素对采样：为每个像素随机采一个邻近像素（在 max_pixel_shift_ratio 控制的偏移范围内），并把偏移后的坐标 clamp 到图像边界。
取邻居深度：在随机邻居位置分别取 shifted_depth 和 shifted_prior_depth。
计算差值：计算深度差 diff = (depth - shifted_depth)/scene_extent 和先验差 prior_diff = (prior_depth - shifted_prior_depth)/scene_extent。
可选符号化先验：若 normalize_loss=True，把 prior_diff 除以其绝对值（detach 后）以近似保留符号、去掉幅值影响。
顺序惩罚规则：计算 diff * prior_diff，若为正（顺序一致）则 loss=0；若为负（顺序相反）则产生正惩罚：-(diff*prior_diff).clamp(max=0)。
可选鲁棒化：若 log_space=True，对惩罚做 log(1 + log_scale * loss) 压缩，减弱极大误差的影响。
输出聚合：按 reduction 选择返回均值（mean）、总和（sum）或逐像素图（none）；debug=True 额外返回中间变量。
`def compute_depth_order_loss(
depth: torch.Tensor,
prior_depth: torch.Tensor,
scene_extent: float = 1.,
max_pixel_shift_ratio: float = 0.05,
normalize_loss: bool = True,
log_space: bool = False,
log_scale: float = 20.,
reduction: str = "mean",
debug: bool = False,
):
height, width = depth.squeeze().shape
pixel_coords = torch.stack(torch.meshgrid(torch.linspace(0, height - 1, height, dtype=torch.long, device=depth.device),torch.linspace(0, width - 1, width, dtype=torch.long, device=depth.device),indexing='ij'
), dim=-1).view(-1, 2)# Get random pixel shifts
# TODO: Change the sampling so that shifts of (0, 0) are not possible
max_pixel_shift = max(round(max_pixel_shift_ratio * max(height, width)), 1)
pixel_shifts = torch.randint(-max_pixel_shift, max_pixel_shift + 1, pixel_coords.shape, device=depth.device)# Apply pixel shifts to pixel coordinates and clamp to image boundaries
shifted_pixel_coords = (pixel_coords + pixel_shifts).clamp(min=torch.tensor([0, 0], device=depth.device),max=torch.tensor([height - 1, width - 1], device=depth.device)
)# Get depth values at shifted pixel coordinates
shifted_depth = depth.squeeze()[shifted_pixel_coords[:, 0],shifted_pixel_coords[:, 1]
].reshape(depth.shape)
shifted_prior_depth = prior_depth.squeeze()[shifted_pixel_coords[:, 0],shifted_pixel_coords[:, 1]
].reshape(depth.shape)# Compute pixel-wise depth order loss
diff = (depth - shifted_depth) / scene_extent
prior_diff = (prior_depth - shifted_prior_depth) / scene_extent
if normalize_loss:prior_diff = prior_diff / prior_diff.detach().abs().clamp(min=1e-8)
depth_order_loss = - (diff * prior_diff).clamp(max=0)
if log_space:depth_order_loss = torch.log(1. + log_scale * depth_order_loss)# Reduce the loss
if reduction == "mean":depth_order_loss = depth_order_loss.mean()
elif reduction == "sum":depth_order_loss = depth_order_loss.sum()
elif reduction == "none":pass
else:raise ValueError(f"Invalid reduction: {reduction}")if debug:return {"depth_order_loss": depth_order_loss,"diff": diff,"prior_diff": prior_diff,"shifted_depth": shifted_depth,"shifted_prior_depth": shifted_prior_depth,}
return depth_order_loss`