不同之处在于torch.nn.functional.mse_loss（x1，x2）在计算平方误差时不对坐标应用求和运算。然而，torch.nn.functional.pairwise_distance和np.linalg.norm对坐标应用求和运算。可以通过以下方式重现计算的mse值：

import torch
import numpy as np
x1 = torch.rand(10,2)
x2 = torch.rand(10,2)
mse_torch = torch.nn.functional.mse_loss(x1,x2)
print(mse_torch) # 0.1557
mse_manual = 0
x3 = torch.zeros(10,2)
for i in range(len(x1)) :
   x3[i,:1] +=(torch.nn.functional.pairwise_distance(x1[i,:1],x2[i,:1],eps=0.0)**2)/len(x1)
   x3[i,1:] += (torch.nn.functional.pairwise_distance(x1[i,1:],x2[i,1:],eps=0.0)**2)/len(x1)
   mse_manual += x3[i]
print(mse_manual.mean()) # 0.1557
mse_manual = 0
for i in range(len(x1)) :
   mse_manual += np.square(x1[i]-x2[i]) / len(x1)
print(mse_manual.mean()) # 0.1557

或者，如果你想使用修改的MSE损失来重现成对距离函数，你可以通过以下方式来实现：

import torch
import numpy as np
# Think of x1 as predicted 2D coordinates and x2 of ground truth
x1 = torch.rand(10,2)
x2 = torch.rand(10,2)
mse_torch = torch.nn.functional.mse_loss(x1,x2, reduction='none')
print(mse_torch.sum(-1).mean()) # 0.3314
mse_direct = 
torch.nn.functional.pairwise_distance(x1,x2).square().mean()
print(mse_direct) # 0.3314
mse_manual = 0
for i in range(len(x1)) :
    mse_manual += np.square(np.linalg.norm(x1[i]-x2[i])) / len(x1)
print(mse_manual) # 0.3314