Question

我定义了四个张量，分别表示index_x，index_y，index_z和value，并使用这三个索引将值分配给新张量。为什么两个作业的结果不同？

import torch
import numpy as np
import random
import os

def seed_torch(seed=0):
    random.seed(seed)
    np.random.seed(seed)
    os.environ['PYTHONHASHSEED'] = str(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.benchmark = False

seed_torch(1)
a_list, b_list, c_list = [], [], []
for i in range(0, 512*512):
    a_ = random.randint(0, 399)
    b_ = random.randint(0, 399)
    c_ = random.randint(0, 199)
    a_list.append(a_)
    b_list.append(b_)
    c_list.append(c_)
a = torch.tensor(a_list)
b = torch.tensor(b_list)
c = torch.tensor(c_list)
v = torch.rand(512*512)
matrix1 = torch.zeros(400,400,200)
matrix2 = torch.zeros(400,400,200)
index=[a,b,c]
matrix1[index]=v
matrix2[index]=v
m = matrix1 - matrix2
print(m.sum())

print（m.sum（））不为零

Answer 1

无法添加评论，但是当我运行您的确切代码时，它会在我的计算机上返回tensor(0.)，因此似乎可以正常工作。

也只是技巧，而不是for循环

a_list, b_list, c_list = [], [], []
for i in range(0, 512*512):
    a_ = random.randint(0, 399)
    b_ = random.randint(0, 399)
    c_ = random.randint(0, 199)
    a_list.append(a_)
    b_list.append(b_)
    c_list.append(c_)
a = torch.tensor(a_list)
b = torch.tensor(b_list)
c = torch.tensor(c_list)

您也可以这样做：

a = torch.randint(400, (512*512,))
b = torch.randint(400, (512*512,))
c = torch.randint(200, (512*512,))

如何使用索引将值分配给张量

1 个答案: