使用预定义的Pytorch函数torch.index_select使用索引列表选择Tensor元素在计算上更有效：

a = [[4, 2, 1, 6], [1, 2, 3, 8], [92, 4, 23, 54]]
tensor_a = torch.tensor(a)
list_indices = [[0,  0], [0, 2], [1, 2]]

# Convert list_indices to Tensor.
indices = torch.tensor(list_indices)

# Get elements from 'tensor_a' using indices.
tensor_a = torch.index_select(tensor_a, 0, indices.view(-1))
print(tensor_a)

如果你希望结果是一个列表而不是一个Tensor，你可以将tensor_a转换为一个列表：

tensor_a_list = tensor_a.tolist()

为了测试计算效率，我创建了1，000，000个索引并比较了执行时间。使用循环比使用我建议的PyTorch方法花费更多的时间：

import time
import torch
start_time = time.time()
a = [[4, 2, 1, 6], [1, 2, 3, 8], [92, 4, 23, 54]]
tensor_a = torch.tensor(a)
indices = torch.randint(0, 2, (1000000,)).tolist()
for indi in indices:
   combos.append(tensor_a[indi])
print("--- %s seconds ---" % (time.time() - start_time))
--- 3.3966853618621826 seconds ---

start_time = time.time()
indices = torch.tensor(indices)
tensor_a=torch.index_select(tensor_a, 0, indices)
print("--- %s seconds ---" % (time.time() - start_time))
--- 0.10641193389892578 seconds ---