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import torch
class LossManager:
def __init__(self):
pass
def position_loss(self, outputs):
"""
计算流型损失的逻辑
:param outputs: 模型的输出
:return: 计算得到的流型损失值
"""
# 计算流型损失(这里使用均方误差作为示例)
manifold_loss = (outputs.abs()).mean() # 计算流型损失
return manifold_loss
def normals_loss(self, cur_data: torch.Tensor, mnfld_pnts: torch.Tensor, all_fi: torch.Tensor, patch_sup: bool) -> torch.Tensor:
"""
计算法线损失
:param cur_data: 当前数据,包含法线信息
:param mnfld_pnts: 流型点
:param all_fi: 所有流型预测值
:param patch_sup: 是否支持补丁
:return: 计算得到的法线损失
"""
# 提取法线
normals = cur_data[:, -self.d_in:]
# 计算分支梯度
branch_grad = gradient(mnfld_pnts, all_fi[:, 0]) # 计算分支梯度
# 计算法线损失
normals_loss = (((branch_grad - normals).abs()).norm(2, dim=1)).mean() # 计算法线损失
return self.normals_lambda * normals_loss # 返回加权后的法线损失
def eikonal_loss(self, nonmnfld_pnts, nonmnfld_pred_all):
"""
计算Eikonal损失
"""
grad_loss_h = torch.zeros(1).cuda() # 初始化 Eikonal 损失
single_nonmnfld_grad = gradient(nonmnfld_pnts, nonmnfld_pred_all[:,0]) # 计算非流形点的梯度
eikonal_loss = ((single_nonmnfld_grad.norm(2, dim=-1) - 1) ** 2).mean() # 计算 Eikonal 损失
return eikonal_loss
def offsurface_loss(self, nonmnfld_pnts, nonmnfld_pred_all):
"""
Eo
惩罚远离表面但是预测值接近0的点
"""
offsurface_loss = torch.exp(-100.0 * torch.abs(nonmnfld_pred_all[:,0])).mean() # 计算离表面损失
return offsurface_loss
def consistency_loss(self, mnfld_pnts, mnfld_pred_all, all_fi):
"""
惩罚流形点预测值和非流形点预测值不一致的点
"""
mnfld_consistency_loss = (mnfld_pred - all_fi[:,0]).abs().mean() # 计算流形一致性损失
return mnfld_consistency_loss
def compute_loss(self, outputs):
"""
计算流型损失的逻辑
:param outputs: 模型的输出
:return: 计算得到的流型损失值
"""
# 计算流型损失(这里使用均方误差作为示例)
manifold_loss = (outputs.abs()).mean() # 计算流型损失
return manifold_loss