window pytorch unet代码学习之random_split_sum of input lengths does not equal the length of

先看看调用

train, val = random_split(dataset, [n_train, n_val])

def random_split(dataset, lengths):
    r"""
    Randomly split a dataset into non-overlapping new datasets of given lengths.
    Arguments:
        dataset (Dataset): Dataset to be split
        lengths (sequence): lengths of splits to be produced
    """
    if sum(lengths) != len(dataset):
        raise ValueError("Sum of input lengths does not equal the length of the input dataset!")
 
    indices = randperm(sum(lengths)).tolist()
    return [Subset(dataset, indices[offset - length:offset]) for offset, length in zip(_accumulate(lengths), lengths)]

从英文字面意思是，将dataset随机的分割为无覆盖的datasets，按照给定的形状，例如这里是[n_train,n_val]分成两组，一组由n_train个数据，一组datasets有n_val个数据。并且取得的数据是随机的。随机从dataset里面无放回的取出n_train和n_val个数据，这样就不会重复了

第一个判断就是这个lengths里面的数据和跟dataset的len是不是一样的，因为不管分多少组，取的数据就len（dataset）这么大。

接下来看randperm，生成1~n之间整数的无重复随机排列。生成1~len（dataset）的数列并转换为list

最后subset，

先看一个for循环

Subset(dataset, indices[offset - length:offset]) for offset, length in zip(_accumulate(lengths), lengths)

for offset，length in zip（。。。。）

查看_accumulate

def _accumulate(iterable, fn=lambda x, y: x + y):
    'Return running totals'
    # _accumulate([1,2,3,4,5]) --> 1 3 6 10 15
    # _accumulate([1,2,3,4,5], operator.mul) --> 1 2 6 24 120
    it = iter(iterable)
    try:
        total = next(it)
    except StopIteration:
        return
    yield total
    for element in it:
        total = fn(total, element)
        yield total

 从示例来看，offset应该是n_train,n_train+n_val，length是n_train，n_val

所以indices就是0：n_train第一个，n_train：n_train+n_val，刚刚好将indices分成两段。

总的来说是调用了两次创建了两个subset放到了list并返回，这个时候得到

然后看subset

class Subset(Dataset):
    r"""
    Subset of a dataset at specified indices.
    Arguments:
        dataset (Dataset): The whole Dataset
        indices (sequence): Indices in the whole set selected for subset
    """
    def __init__(self, dataset, indices):
        self.dataset = dataset
        self.indices = indices

这个很简单只是简单的复制操作而已，