paddle 中 torch.cdist 实现

def paddle_cdist(x, y, p=2):
    x_len = x.shape[0]
    y_len = y.shape[0]

    out = []
    for x_ix, x_item in enumerate(x):
        for y_ix, y_item in enumerate(y):
            item = paddle.dist(x_item, y_item, p)
            out.append( item )
    out = paddle.concat(out)
    return out.reshape([x_len, y_len])

Paddle结果

import numpy as np

np.random.seed(1107)
x = np.random.rand(4, 4)
np.random.seed(1107*2)
y = np.random.rand(2, 4)

x = paddle.to_tensor(x)
y = paddle.to_tensor(y)
out = paddle_cdist(x, y, 2)
print(out) # out = [1.]

Tensor(shape=[4, 2], dtype=float64, place=Place(gpu:0), stop_gradient=True,
       [[0.86650367, 0.97836384],
        [0.37499482, 0.76791689],
        [0.74998959, 0.82276324],
        [0.80922280, 0.44565161]])

torch 结果

import numpy as np

np.random.seed(1107)
x = np.random.rand(4, 4)
np.random.seed(1107*2)
y = np.random.rand(2, 4)

x = torch.as_tensor(x)
y = torch.as_tensor(y)
out = torch.cdist(x, y, 2)
print(out) # out = [1.]

tensor([[0.8665, 0.9784],
        [0.3750, 0.7679],
        [0.7500, 0.8228],
        [0.8092, 0.4457]], dtype=torch.float64)

来顺便看看 torch.cdist 到底还做了个啥：

import torch 

a = torch.tensor([[0.9041,  0.0196], 
                  [-0.3108, -2.4423], 
                  [-0.4821,  1.059]])
b = torch.tensor([[-2.1763, -0.4713], 
                  [-0.6986,  1.3702]])
c = torch.cdist(a, b, p=2)
print(c)

tensor([[3.1193, 2.0959],
        [2.7138, 3.8322],
        [2.2830, 0.3791]])

res = torch.zeros([a.shape[0],  b.shape[0]])
for a_ix, a_item in enumerate(a):
    for b_ix, b_item in enumerate(b):
        res[a_ix][b_ix] = torch.norm((a_item - b_item), p=2)       
print(res)

tensor([[3.1193, 2.0959],
        [2.7138, 3.8322],
        [2.2830, 0.3791]])