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52 lines
1.6 KiB
52 lines
1.6 KiB
import torch
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class LossManager:
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def __init__(self):
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pass
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def position_loss(self, outputs):
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"""
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计算流型损失的逻辑
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:param outputs: 模型的输出
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:return: 计算得到的流型损失值
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"""
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# 计算流型损失（这里使用均方误差作为示例）
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manifold_loss = (outputs.abs()).mean() # 计算流型损失
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return manifold_loss
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def normals_loss(self, cur_data: torch.Tensor, mnfld_pnts: torch.Tensor, all_fi: torch.Tensor, patch_sup: bool) -> torch.Tensor:
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"""
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计算法线损失
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:param cur_data: 当前数据，包含法线信息
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:param mnfld_pnts: 流型点
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:param all_fi: 所有流型预测值
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:param patch_sup: 是否支持补丁
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:return: 计算得到的法线损失
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"""
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# 提取法线
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normals = cur_data[:, -self.d_in:]
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# 计算分支梯度
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branch_grad = gradient(mnfld_pnts, all_fi[:, 0]) # 计算分支梯度
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# 计算法线损失
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normals_loss = (((branch_grad - normals).abs()).norm(2, dim=1)).mean() # 计算法线损失
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return self.normals_lambda * normals_loss # 返回加权后的法线损失
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def compute_loss(self, outputs):
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"""
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计算流型损失的逻辑
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:param outputs: 模型的输出
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:return: 计算得到的流型损失值
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"""
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# 计算流型损失（这里使用均方误差作为示例）
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manifold_loss = (outputs.abs()).mean() # 计算流型损失
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return manifold_loss
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