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python调用迭代器里面的内容

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迭代器 Iterater

python要查看迭代器离得部分内容时，主要的有以下3种方法：(以查看第一个元素为例)

1.for循环

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dev_iter = build_iterator(dev_data, config)
for i in dev_iter:
    if i:
        print(i)
        break

结果：

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((tensor([[ 101, 1036, 4997,  ...,    0,    0,    0],
        [ 101, 1036, 4997,  ...,    0,    0,    0],
        [ 101, 1036, 4997,  ..., 4851, 4289,    0],
        ...,
        [ 101, 4495, 3189,  ...,  831, 1044, 1355],
        [ 101, 4495, 3189,  ..., 7317, 7164, 7509],
        [ 101, 7922, 6235,  ..., 6804, 1931,    0]], device='cuda:0'), tensor([25, 29, 31, 32, 32, 32, 32, 32, 32, 29, 32, 32, 29, 20, 32, 29, 32, 32,
        30, 31, 31, 32, 31, 29, 32, 32, 32, 32, 28, 30, 30, 32, 32, 31, 27, 32,
        31, 30, 31, 32, 30, 32, 29, 30, 31, 32, 27, 25, 29, 32, 32, 32, 31, 30,
        30, 31, 31, 22, 32, 31, 29, 32, 32, 31], device='cuda:0'), tensor([[1, 1, 1,  ..., 0, 0, 0],
        [1, 1, 1,  ..., 0, 0, 0],
        [1, 1, 1,  ..., 1, 1, 0],
        ...,
        [1, 1, 1,  ..., 1, 1, 1],
        [1, 1, 1,  ..., 1, 1, 1],
        [1, 1, 1,  ..., 1, 1, 0]], device='cuda:0')), tensor([8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3,
        3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 7, 7, 7,
        7, 7, 7, 7, 7, 7, 4, 4, 4, 4, 4, 4, 4, 4, 4, 6], device='cuda:0'))

2.生成式/生成器

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dev_iter = build_iterator(dev_data, config)
[_ for _ in dev_iter][0]

结果：

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((tensor([[ 101, 1036, 4997,  ...,    0,    0,    0],
          [ 101, 1036, 4997,  ...,    0,    0,    0],
          [ 101, 1036, 4997,  ..., 4851, 4289,    0],
          ...,
          [ 101, 4495, 3189,  ...,  831, 1044, 1355],
          [ 101, 4495, 3189,  ..., 7317, 7164, 7509],
          [ 101, 7922, 6235,  ..., 6804, 1931,    0]], device='cuda:0'),
  tensor([25, 29, 31, 32, 32, 32, 32, 32, 32, 29, 32, 32, 29, 20, 32, 29, 32, 32,
          30, 31, 31, 32, 31, 29, 32, 32, 32, 32, 28, 30, 30, 32, 32, 31, 27, 32,
          31, 30, 31, 32, 30, 32, 29, 30, 31, 32, 27, 25, 29, 32, 32, 32, 31, 30,
          30, 31, 31, 22, 32, 31, 29, 32, 32, 31], device='cuda:0'),
  tensor([[1, 1, 1,  ..., 0, 0, 0],
          [1, 1, 1,  ..., 0, 0, 0],
          [1, 1, 1,  ..., 1, 1, 0],
          ...,
          [1, 1, 1,  ..., 1, 1, 1],
          [1, 1, 1,  ..., 1, 1, 1],
          [1, 1, 1,  ..., 1, 1, 0]], device='cuda:0')),
 tensor([8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3,
         3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 7, 7, 7,
         7, 7, 7, 7, 7, 7, 4, 4, 4, 4, 4, 4, 4, 4, 4, 6], device='cuda:0'))

3.next()函数

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dev_iter = build_iterator(dev_data, config)
next(dev_iter)

结果：

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((tensor([[ 101, 1036, 4997,  ...,    0,    0,    0],
          [ 101, 1036, 4997,  ...,    0,    0,    0],
          [ 101, 1036, 4997,  ..., 4851, 4289,    0],
          ...,
          [ 101, 4495, 3189,  ...,  831, 1044, 1355],
          [ 101, 4495, 3189,  ..., 7317, 7164, 7509],
          [ 101, 7922, 6235,  ..., 6804, 1931,    0]], device='cuda:0'),
  tensor([25, 29, 31, 32, 32, 32, 32, 32, 32, 29, 32, 32, 29, 20, 32, 29, 32, 32,
          30, 31, 31, 32, 31, 29, 32, 32, 32, 32, 28, 30, 30, 32, 32, 31, 27, 32,
          31, 30, 31, 32, 30, 32, 29, 30, 31, 32, 27, 25, 29, 32, 32, 32, 31, 30,
          30, 31, 31, 22, 32, 31, 29, 32, 32, 31], device='cuda:0'),
  tensor([[1, 1, 1,  ..., 0, 0, 0],
          [1, 1, 1,  ..., 0, 0, 0],
          [1, 1, 1,  ..., 1, 1, 0],
          ...,
          [1, 1, 1,  ..., 1, 1, 1],
          [1, 1, 1,  ..., 1, 1, 1],
          [1, 1, 1,  ..., 1, 1, 0]], device='cuda:0')),
 tensor([8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3,
         3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 7, 7, 7,
         7, 7, 7, 7, 7, 7, 4, 4, 4, 4, 4, 4, 4, 4, 4, 6], device='cuda:0'))