背景
numpy 中的reshape函数蕴含一个order变量，默认order='C'，即在变形中以后面的维度（行）为优先程序重新排列元素，而order='F'时以前面的维度（列）为优先程序重新排列元素，官网文档中给出了示例：
>>> np.reshape(a, (2, 3)) # C-like index orderingarray([[0, 1, 2], [3, 4, 5]])>>> np.reshape(np.ravel(a), (2, 3)) # equivalent to C ravel then C reshapearray([[0, 1, 2], [3, 4, 5]])>>> np.reshape(a, (2, 3), order='F') # Fortran-like index orderingarray([[0, 4, 3], [2, 1, 5]])>>> np.reshape(np.ravel(a, order='F'), (2, 3), order='F')array([[0, 4, 3], [2, 1, 5]])pytorch解决方案
在pytorch中，torch.reshape()函数只承受矩阵和形态两个参数，采纳了行优先(C-Style)的变换形式，如果须要应用列优先的变换，须要借助permute()函数，stackoverflow上给出了解决方案：
def reshape_fortran(x, shape): if len(x.shape) > 0: x = x.permute(*reversed(range(len(x.shape)))) return x.reshape(*reversed(shape)).permute(*reversed(range(len(shape))))性能测试
然而下面的作者狐疑permute()函数外部依然会创立张量的正本，影响效率。因而笔者对这种办法做了测试，并与numpy的内置函数做了比照。测试环境为i9-10900X/RTX2080Ti。
测试代码：
import numpy as npimport torchimport timedim1 = 40dim2 = 50dim3 = 5def reshape_fortran(x, shape): if len(x.shape) > 0: x = x.permute(*reversed(range(len(x.shape)))) return x.reshape(*reversed(shape)).permute(*reversed(range(len(shape))))torch.cuda.set_device(0)device = torch.device('cuda')x = [torch.from_numpy(np.random.rand(dim1, dim2)).to(device) for _ in range(100)]xx = [torch.from_numpy(np.random.rand(dim1, dim2)).to(device) for _ in range(100)]for i in range(100): y = x[i].reshape([dim2, dim1])# c reshapet0 = time.time()for i in range(100): y = xx[i].reshape([dim2, dim3, -1])t1 = time.time()# fortran reshapefor i in range(100): yy = reshape_fortran(xx[i], [dim2, dim3, -1])t2 = time.time()print(f'torch build-in reshape: {(t1 - t0)/100} s')print(f'torch permute reshape: {(t2 - t1)/100} s')x = [np.random.rand(dim1, dim2) for _ in range(100)]xx = [np.random.rand(dim1, dim2) for _ in range(100)]for i in range(100): y = x[i].reshape([dim2, dim3, -1])t0 = time.time()for i in range(100): yy = xx[i].reshape([dim2, dim3, -1])t1 = time.time()for i in range(100): yyy = xx[i].reshape([dim2, dim3, -1], order='F')t2 = time.time()print(f'numpy C reshape: {(t1 - t0)/100} s')print(f'numpy F reshape: {(t2 - t1)/100} s')测试后果：
torch build-in reshape: 9.72747802734375e-07 storch permute reshape: 1.1897087097167968e-05 snumpy C reshape: 3.0517578125e-07 snumpy F reshape: 2.474784851074219e-06 s测试中pytorch中基于permute()的办法的耗时是内置行优先reshape()函数的10倍，然而在numpy的测试中，列优先变换的耗时也是行优先的10倍。因而能够认为在pytorch中，基于permute()函数的变换计算效率很高，不须要持续优化。
参考文献
numpy文档
stackoverflow原问题