python切片用法个人总结

在深度学习中进行张量操作的时候经常会遇到这样的切片形式，一开始总会感到头疼，所以现在总结一下用法，欢迎大家进行补充。

data[x:y:z]

data[::-1]

data[:-1]

data[-3:]

data[1:4, 2:4]

data[…, 1:2]

data[:, None]

这是最近在看的代码，进行loss的计算，用到了很多切片的知识，所以进行总结，希望看完这篇博客以后大家也能看懂类似的代码

def compute_loss(p, targets, model):  # predictions, targets, model
    ft = torch.cuda.FloatTensor if p[0].is_cuda else torch.Tensor
    lxy, lwh, lcls, lconf = ft([0]), ft([0]), ft([0]), ft([0])
    # build_targets对targets向量进行处理
    txy, twh, tcls, indices = build_targets(model, targets)

    # Define criteria
    MSE = nn.MSELoss()
    CE = nn.CrossEntropyLoss()  # (weight=model.class_weights)
    BCE = nn.BCEWithLogitsLoss()

    # Compute losses
    h = model.hyp  # hyperparameters
    bs = p[0].shape[0]  # batch size
    k = bs  # loss gain
    for i, pi0 in enumerate(p):  # layer i predictions, i
        b, a, gj, gi = indices[i]  # image, anchor, gridy, gridx
        tconf = torch.zeros_like(pi0[..., 0])  # conf

        # Compute losses
        if len(b):  # number of targets
            pi = pi0[b, a, gj, gi]  # predictions closest to anchors
            tconf[b, a, gj, gi] = 1  # conf
            # pi[..., 2:4] = torch.sigmoid(pi[..., 2:4])  # wh power loss (uncomment)

            lxy += (k * h['xy']) * MSE(torch.sigmoid(pi[..., 0:2]), txy[i])  # xy loss
            lwh += (k * h['wh']) * MSE(pi[..., 2:4], twh[i])  # wh yolo loss
            lcls += (k * h['cls']) * CE(pi[..., 5:], tcls[i])  # class_conf loss

        # pos_weight = ft([gp[i] / min(gp) * 4.])
        # BCE = nn.BCEWithLogitsLoss(pos_weight=pos_weight)
        lconf += (k * h['conf']) * BCE(pi0[..., 4], tconf)  # obj_conf loss
    loss = lxy + lwh + lconf + lcls

    return loss, torch.cat((lxy, lwh, lconf, lcls, loss)).detach()

1. 单列表切片

对于一个普遍形式：data[x:y:z] 讲一下他的意义：

The syntax [x:y:z] means "take every zth element of a list from index x to index y". When z is negative, it indicates going backwards. When x isn’t specified, it defaults to the first element of the list in the direction you are traversing the list. When y isn’t specified, it defaults to the last element of the list. So if we want to take every 2th element of a list, we use [::2].

– from github:python-is-cool

翻译过来就是：x是起始坐标，y是结束坐标（不包含），z是代表每隔z个数取一个数,如果z是负数代表方向是从后往前。

看一个例子：

>>> data = list(range(10))
>>> data
[0, 1, 2, 3, 4, 5, 6, 7, 8