tensor.stride()
Stride is the jump necessary to go from one element to the next one in the specified dimension dim.
一个元素到另一个元素，元素粒度
任意维度上的步长，是其低维度乘积。
shape: (12, 512, 768) stride: (512x768x1, 768x1, 1x1)
tensor.as_strided()
-
input (Tensor) – the input tensor.
-
size (tuple or ints) – the shape of the output tensor
-
stride (tuple or ints) – the stride of the output tensor
-
storage_offset (int, optional) – the offset in the underlying storage of the output tensor
由input张量，以指定的size, stride, storage_offset创建新的view。
import torch
tensor.stride
x = torch.randn(3, 2, 4)
x
tensor([[[ 0.9071, 0.2834, -0.1989, -1.4063],
[-1.7204, 1.7097, -1.2490, 1.1157]],
[[ 1.7791, 0.6694, 0.3891, -0.0156],
[-1.6843, -1.1728, 0.0408, -0.7561]],
[[ 0.9002, 1.4651, 0.7972, 1.1046],
[ 1.4144, 0.3738, 0.4680, 0.9603]]])
print(f"0维度上的步长：{len(x[0]) * len(x[0][0] * 1)}\n"
f"1维度上的步长：{len(x[0][0]) * 1}\n"
f"2维度上的步长：{1 * 1}")
x.stride()
0维度上的步长：8
1维度上的步长：4
2维度上的步长：1
(8, 4, 1)
x[0]
tensor([[ 0.9071, 0.2834, -0.1989, -1.4063],
[-1.7204, 1.7097, -1.2490, 1.1157]])
x[0][0]
tensor([ 0.9071, 0.2834, -0.1989, -1.4063])
x[0][0][0]
tensor(0.9071)
x.shape
torch.Size([3, 2, 4])
x1 = torch.randn(2, 3, 5) # stride: (3 * 5 * 1, 5 * 1, 1 * 1)
print(x1)
x1.stride()
tensor([[[-0.2049, 1.7724, -0.4653, 0.2304, -0.6612],
[-0.4517, -1.1064, -1.3647, -0.7284, 0.2986],
[ 0.4436, -0.0191, 2.6322, 0.2686, 0.7015]],
[[ 1.6355, 0.7517, -0.9918, 0.2702, 1.8537],
[ 0.2296, -1.2191, 0.1392, -0.7129, -0.9681],
[-1.5700, 0.2363, 0.3035, 0.7965, 1.3703]]])
(15, 5, 1)
x2 = torch.randn(10, 15, 256, 64) # stride: (15 * 256 * 64 * 1, 256 * 64 * 1, 64 * 1, 1 *1)
print((15 * 256 * 64 * 1, 256 * 64 * 1, 64 * 1, 1 *1))
x2.stride()
(245760, 16384, 64, 1)
(245760, 16384, 64, 1)
tensor.as_strided
x1.as_strided((2,2,2), (16,10,4))
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
/tmp/ipykernel_23801/2376004043.py in <module>
----> 1 x1.as_strided((2,2,2), (16,10,4))
RuntimeError: setStorage: sizes [2, 2, 2], strides [16, 10, 4], storage offset 0, and itemsize 4 requiring a storage size of 124 are out of bounds for storage of size 120
h = torch.randn(12,8,512,64)
h.stride()
(262144, 32768, 64, 1)
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