重塑 4D/5D 阵列的技巧或模式，(视频到帧) [英] Tips or patterns for reshaping 4D/5D arrays, (videos to frames)
问题描述
我发现很难在 numpy/pytorch 中可视化重塑 4D 5D 数组.(我假设两者都以类似的方式重塑，我目前正在使用 pytorch！).
I find it really hard to visualize reshaping 4D 5D arrays in numpy/pytorch. (I assume both reshape in similar patter, I am using pytorch currently!).
假设我有尺寸为 [N x C x D x H x W] 的视频
Like suppose I have videos with dimension [N x C x D x H x W]
(数量视频 x 频道视频 x 帧视频 x 高度视频 x 宽度视频)
(num videos x channels video x frames video x height video x width video)
假设我想将视频重塑为 [N x C x H x W] 的帧，我应该如何进行重塑.
Suppose I want to reshape video into frames as [N x C x H x W], how should I proceed in reshape.
简单地应用 x = x.reshape(N*D, C, H, W) 实际上并没有做到，它给出了错误的元素顺序.
Simple applying x = x.reshape(N*D, C, H, W) doesn't actually do it, it gives wrong order of elements.
你能帮助我了解如何做到这一点，以及你对模式的任何直觉吗?
Can you help me with how to do this, and any slight of intuition of pattern you used?
顺便说一句，如果我有一个视频(即假设我使用 1x3x100x256x256:
On a sidenote, if i have one video (i.e suppose 1x3x100x256x256 I use :
以下代码方法:
x = x.squeeze(0).T.reshape((100,3,256,256))[:,:,None,:,:] 并且它有效
太好了.无法找出超过 1 个视频.
great. Couldnt figure out for more than 1 video.
谢谢！
根据要求:
input = np.random.randn(N,C,D,H,W)
output = np.zeros((N*D,C,H,W))
根据请求，一个基于 for 循环的代码来显示我想要的<代码>对于范围内的 h(N):对于范围(D)中的我:对于范围内的 j(C):对于范围内的 k (H):对于范围内的 l(W):输出[h*D + i,j,k,l] = 输入[h,j,i,k,l]
As per the request, a for loop based code to show what I want
for h in range(N):
for i in range(D):
for j in range(C):
for k in range(H):
for l in range(W):
output[h*D + i,j,k,l] = input[h,j,i,k,l]
推荐答案
简单地交换第二和第三个轴，然后将新的第二个轴(旧的第三个)与第一个进行整形合并 -
Simply swap the second and third axes, and then merge the new second axis (old third one) with the first one with reshaping -
output = input_array.swapaxes(1,2).reshape(N*D,C,H,W)
我们也可以使用 transpose : input_array.transpose(0,2,1,3,4) 来获得相同的交换轴效果.
We can also use transpose : input_array.transpose(0,2,1,3,4) to get the same swapping axes effect.
一般的直观方法请参考在 NumPy 中将 4D 数组重塑为 2D 数组背后的直觉和想法.
For a general intuitive method, please refer to Intuition and idea behind reshaping 4D array to 2D array in NumPy.
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