当我们重塑 numpy 数组时，如何推断步幅大小？
when we reshape numpy array, how is the stride size inferred?
我有一个 1x1024 一维数组（扁平图像）。要查看图像，我想将其大小重塑为 32x32。
我可以通过 x.reshape(-1,32) 轻松实现这一目标，并且它按我的预期工作。它不会破坏图像。它每次读取宽度为 32 的一维数组。
说这一次，有 4 张图像，大小为 32 x 8。重塑它的安全方法是什么？
步幅定义背后的逻辑是什么？它总是从最大的维度开始（比如，3d->2d->1d）吗？好像是..
In [2]: a = np.arange(1024)
In [3]: a.reshape(4,32,8)
Out[3]:
array([[[ 0, 1, 2, ..., 5, 6, 7],
[ 8, 9, 10, ..., 13, 14, 15],
[ 16, 17, 18, ..., 21, 22, 23],
...,
[ 232, 233, 234, ..., 237, 238, 239],
[ 240, 241, 242, ..., 245, 246, 247],
[ 248, 249, 250, ..., 253, 254, 255]],
[[ 256, 257, 258, ..., 261, 262, 263],
[ 264, 265, 266, ..., 269, 270, 271],
[ 272, 273, 274, ..., 277, 278, 279],
...,
[ 488, 489, 490, ..., 493, 494, 495],
[ 496, 497, 498, ..., 501, 502, 503],
[ 504, 505, 506, ..., 509, 510, 511]],
[[ 512, 513, 514, ..., 517, 518, 519],
[ 520, 521, 522, ..., 525, 526, 527],
[ 528, 529, 530, ..., 533, 534, 535],
...,
[ 744, 745, 746, ..., 749, 750, 751],
[ 752, 753, 754, ..., 757, 758, 759],
[ 760, 761, 762, ..., 765, 766, 767]],
[[ 768, 769, 770, ..., 773, 774, 775],
[ 776, 777, 778, ..., 781, 782, 783],
[ 784, 785, 786, ..., 789, 790, 791],
...,
[1000, 1001, 1002, ..., 1005, 1006, 1007],
[1008, 1009, 1010, ..., 1013, 1014, 1015],
[1016, 1017, 1018, ..., 1021, 1022, 1023]]])
In [4]: a.reshape(4,-1,8)
Out[4]:
array([[[ 0, 1, 2, ..., 5, 6, 7],
[ 8, 9, 10, ..., 13, 14, 15],
[ 16, 17, 18, ..., 21, 22, 23],
...,
[ 232, 233, 234, ..., 237, 238, 239],
[ 240, 241, 242, ..., 245, 246, 247],
[ 248, 249, 250, ..., 253, 254, 255]],
[[ 256, 257, 258, ..., 261, 262, 263],
[ 264, 265, 266, ..., 269, 270, 271],
[ 272, 273, 274, ..., 277, 278, 279],
...,
[ 488, 489, 490, ..., 493, 494, 495],
[ 496, 497, 498, ..., 501, 502, 503],
[ 504, 505, 506, ..., 509, 510, 511]],
[[ 512, 513, 514, ..., 517, 518, 519],
[ 520, 521, 522, ..., 525, 526, 527],
[ 528, 529, 530, ..., 533, 534, 535],
...,
[ 744, 745, 746, ..., 749, 750, 751],
[ 752, 753, 754, ..., 757, 758, 759],
[ 760, 761, 762, ..., 765, 766, 767]],
[[ 768, 769, 770, ..., 773, 774, 775],
[ 776, 777, 778, ..., 781, 782, 783],
[ 784, 785, 786, ..., 789, 790, 791],
...,
[1000, 1001, 1002, ..., 1005, 1006, 1007],
[1008, 1009, 1010, ..., 1013, 1014, 1015],
[1016, 1017, 1018, ..., 1021, 1022, 1023]]])
In [5]: a.reshape(4,8,32)
Out[5]:
array([[[ 0, 1, 2, ..., 29, 30, 31],
[ 32, 33, 34, ..., 61, 62, 63],
[ 64, 65, 66, ..., 93, 94, 95],
...,
[ 160, 161, 162, ..., 189, 190, 191],
[ 192, 193, 194, ..., 221, 222, 223],
[ 224, 225, 226, ..., 253, 254, 255]],
[[ 256, 257, 258, ..., 285, 286, 287],
[ 288, 289, 290, ..., 317, 318, 319],
[ 320, 321, 322, ..., 349, 350, 351],
...,
[ 416, 417, 418, ..., 445, 446, 447],
[ 448, 449, 450, ..., 477, 478, 479],
[ 480, 481, 482, ..., 509, 510, 511]],
[[ 512, 513, 514, ..., 541, 542, 543],
[ 544, 545, 546, ..., 573, 574, 575],
[ 576, 577, 578, ..., 605, 606, 607],
...,
[ 672, 673, 674, ..., 701, 702, 703],
[ 704, 705, 706, ..., 733, 734, 735],
[ 736, 737, 738, ..., 765, 766, 767]],
[[ 768, 769, 770, ..., 797, 798, 799],
[ 800, 801, 802, ..., 829, 830, 831],
[ 832, 833, 834, ..., 861, 862, 863],
...,
[ 928, 929, 930, ..., 957, 958, 959],
[ 960, 961, 962, ..., 989, 990, 991],
[ 992, 993, 994, ..., 1021, 1022, 1023]]])
reshape 不会对基础值重新排序。该数组存储为一维字节数组，加上 shape、strides 和 dtype 用于 view 它作为特定的多维数组。
可以看看strides属性：
In [513]: arr = np.arange(1024)
In [514]: arr.shape, arr.strides
Out[514]: ((1024,), (8,))
In [515]: arr1=arr.reshape(32,32);arr1.shape, arr1.strides
Out[515]: ((32, 32), (256, 8))
In [516]: arr1=arr.reshape(4,32,8);arr1.shape, arr1.strides
Out[516]: ((4, 32, 8), (2048, 64, 8))
对于 1d，它一次只步进 8 个字节（int64 的大小）
用2d，256=32*8；要遍历行，它必须步进 256 字节
3d, 2048 = 32 * 8 * 8;块之间的步骤。
为了好玩，看看转置：
In [517]: arr1=arr.reshape(4,32,8).T;arr1.shape, arr1.strides
Out[517]: ((8, 32, 4), (8, 64, 2048))
形状反转了，步伐也变了。
很多时候在reshape一个图像数组成block的时候，我们需要reshape成小块，做partial transpose，reshape到一个目标。第一个重塑和转置创建一个视图，只是玩弄形状和步幅。但是最后reshape往往需要copy
我有一个 1x1024 一维数组（扁平图像）。要查看图像，我想将其大小重塑为 32x32。
我可以通过 x.reshape(-1,32) 轻松实现这一目标，并且它按我的预期工作。它不会破坏图像。它每次读取宽度为 32 的一维数组。
说这一次，有 4 张图像，大小为 32 x 8。重塑它的安全方法是什么？ 步幅定义背后的逻辑是什么？它总是从最大的维度开始（比如，3d->2d->1d）吗？好像是..
In [2]: a = np.arange(1024)
In [3]: a.reshape(4,32,8)
Out[3]:
array([[[ 0, 1, 2, ..., 5, 6, 7],
[ 8, 9, 10, ..., 13, 14, 15],
[ 16, 17, 18, ..., 21, 22, 23],
...,
[ 232, 233, 234, ..., 237, 238, 239],
[ 240, 241, 242, ..., 245, 246, 247],
[ 248, 249, 250, ..., 253, 254, 255]],
[[ 256, 257, 258, ..., 261, 262, 263],
[ 264, 265, 266, ..., 269, 270, 271],
[ 272, 273, 274, ..., 277, 278, 279],
...,
[ 488, 489, 490, ..., 493, 494, 495],
[ 496, 497, 498, ..., 501, 502, 503],
[ 504, 505, 506, ..., 509, 510, 511]],
[[ 512, 513, 514, ..., 517, 518, 519],
[ 520, 521, 522, ..., 525, 526, 527],
[ 528, 529, 530, ..., 533, 534, 535],
...,
[ 744, 745, 746, ..., 749, 750, 751],
[ 752, 753, 754, ..., 757, 758, 759],
[ 760, 761, 762, ..., 765, 766, 767]],
[[ 768, 769, 770, ..., 773, 774, 775],
[ 776, 777, 778, ..., 781, 782, 783],
[ 784, 785, 786, ..., 789, 790, 791],
...,
[1000, 1001, 1002, ..., 1005, 1006, 1007],
[1008, 1009, 1010, ..., 1013, 1014, 1015],
[1016, 1017, 1018, ..., 1021, 1022, 1023]]])
In [4]: a.reshape(4,-1,8)
Out[4]:
array([[[ 0, 1, 2, ..., 5, 6, 7],
[ 8, 9, 10, ..., 13, 14, 15],
[ 16, 17, 18, ..., 21, 22, 23],
...,
[ 232, 233, 234, ..., 237, 238, 239],
[ 240, 241, 242, ..., 245, 246, 247],
[ 248, 249, 250, ..., 253, 254, 255]],
[[ 256, 257, 258, ..., 261, 262, 263],
[ 264, 265, 266, ..., 269, 270, 271],
[ 272, 273, 274, ..., 277, 278, 279],
...,
[ 488, 489, 490, ..., 493, 494, 495],
[ 496, 497, 498, ..., 501, 502, 503],
[ 504, 505, 506, ..., 509, 510, 511]],
[[ 512, 513, 514, ..., 517, 518, 519],
[ 520, 521, 522, ..., 525, 526, 527],
[ 528, 529, 530, ..., 533, 534, 535],
...,
[ 744, 745, 746, ..., 749, 750, 751],
[ 752, 753, 754, ..., 757, 758, 759],
[ 760, 761, 762, ..., 765, 766, 767]],
[[ 768, 769, 770, ..., 773, 774, 775],
[ 776, 777, 778, ..., 781, 782, 783],
[ 784, 785, 786, ..., 789, 790, 791],
...,
[1000, 1001, 1002, ..., 1005, 1006, 1007],
[1008, 1009, 1010, ..., 1013, 1014, 1015],
[1016, 1017, 1018, ..., 1021, 1022, 1023]]])
In [5]: a.reshape(4,8,32)
Out[5]:
array([[[ 0, 1, 2, ..., 29, 30, 31],
[ 32, 33, 34, ..., 61, 62, 63],
[ 64, 65, 66, ..., 93, 94, 95],
...,
[ 160, 161, 162, ..., 189, 190, 191],
[ 192, 193, 194, ..., 221, 222, 223],
[ 224, 225, 226, ..., 253, 254, 255]],
[[ 256, 257, 258, ..., 285, 286, 287],
[ 288, 289, 290, ..., 317, 318, 319],
[ 320, 321, 322, ..., 349, 350, 351],
...,
[ 416, 417, 418, ..., 445, 446, 447],
[ 448, 449, 450, ..., 477, 478, 479],
[ 480, 481, 482, ..., 509, 510, 511]],
[[ 512, 513, 514, ..., 541, 542, 543],
[ 544, 545, 546, ..., 573, 574, 575],
[ 576, 577, 578, ..., 605, 606, 607],
...,
[ 672, 673, 674, ..., 701, 702, 703],
[ 704, 705, 706, ..., 733, 734, 735],
[ 736, 737, 738, ..., 765, 766, 767]],
[[ 768, 769, 770, ..., 797, 798, 799],
[ 800, 801, 802, ..., 829, 830, 831],
[ 832, 833, 834, ..., 861, 862, 863],
...,
[ 928, 929, 930, ..., 957, 958, 959],
[ 960, 961, 962, ..., 989, 990, 991],
[ 992, 993, 994, ..., 1021, 1022, 1023]]])
reshape 不会对基础值重新排序。该数组存储为一维字节数组，加上 shape、strides 和 dtype 用于 view 它作为特定的多维数组。
可以看看strides属性：
In [513]: arr = np.arange(1024)
In [514]: arr.shape, arr.strides
Out[514]: ((1024,), (8,))
In [515]: arr1=arr.reshape(32,32);arr1.shape, arr1.strides
Out[515]: ((32, 32), (256, 8))
In [516]: arr1=arr.reshape(4,32,8);arr1.shape, arr1.strides
Out[516]: ((4, 32, 8), (2048, 64, 8))
对于 1d，它一次只步进 8 个字节（int64 的大小）
用2d，256=32*8；要遍历行，它必须步进 256 字节
3d, 2048 = 32 * 8 * 8;块之间的步骤。
为了好玩，看看转置：
In [517]: arr1=arr.reshape(4,32,8).T;arr1.shape, arr1.strides
Out[517]: ((8, 32, 4), (8, 64, 2048))
形状反转了，步伐也变了。
很多时候在reshape一个图像数组成block的时候，我们需要reshape成小块，做partial transpose，reshape到一个目标。第一个重塑和转置创建一个视图，只是玩弄形状和步幅。但是最后reshape往往需要copy