3.Pytorch基础模块torch的API之Indexing,Slicing,Joining,Mutating Ops实例详解
文章目录
-
-
- 0. torch
- 1. Tensors
- 2. Creation Ops
- 3. Indexing,Slicing,Joining,Mutating Ops
-
- 3.1 torch.cat()
- 3.2 torch.chunk()
- 3.3 torch.dstack()
- 3.4 torch.gather()
- 3.5 torch.hstack()
- 3.6 torch.index_select()
- 3.7 torch.masked_select()
- 3.8 torch.movedim()
- 3.9 torch.narrow()
- 3.10 torch.nonzero()
- 3.11 torch.reshape()
- 3.12 torch.split()
- 3.13 torch.squeeze()
- 3.14 torch.stack()
- 3.15 torch.t()
- 3.16 torch.take()
- 3.17 torch.transpose()
- 3.18 torch.unbind()
- 3.19 torch.unsqueeze()
- 3.20 torch.vstack()
- 3.21 torch.where()
- reference:
- 0. torch
-
0. torch
torch包含多维张量的数据结构，并定义了对这些张量的数学运算。
1. Tensors
1.Pytorch基础模块torch的API之Tensors实例详解
2. Creation Ops
2.Pytorch基础模块torch的API之Creation Ops实例详解
3. Indexing,Slicing,Joining,Mutating Ops
import torch
import numpy as np
3.1 torch.cat()
torch.cat(tensors, dim=0, *, out=None) → Tensor
cat是concatenate拼接的意思，这个函数就是拼接tensor
参数dim是指定拼接维度，0是按行拼接，1是按列拼接
注意：既然是张量拼接，对两个张量也是有要求的，
比如dim=0按行拼接时，需要两个张量列相等；dim=1按列拼接时，需要两个张量行相等
# 按行拼接，a,b 的列都是3
a = torch.ones(2, 3)
b = torch.tensor([[1.2, 1.6, 1.8],
[2.4, 2.6, 2.8],
[3, 8, 10]])
c = torch.cat((a, b), 0)
c
tensor([[ 1.0000, 1.0000, 1.0000],
[ 1.0000, 1.0000, 1.0000],
[ 1.2000, 1.6000, 1.8000],
[ 2.4000, 2.6000, 2.8000],
[ 3.0000, 8.0000, 10.0000]])
# 按行拼接，a,d 的行都是2
d = torch.tensor([[1.2, 1.6, 1.8, 2.0],
[2.4, 2.6, 2.8, 3.0]])
e = torch.cat((a, d), 1)
e
tensor([[1.0000, 1.0000, 1.0000, 1.2000, 1.6000, 1.8000, 2.0000],
[1.0000, 1.0000, 1.0000, 2.4000, 2.6000, 2.8000, 3.0000]])
3.2 torch.chunk()
torch.chunk(input, chunks, dim=0) → List of Tensors
将一个张量分解成特定数量的块。
如果给定维度dim的张量大小不能被chunks整除，最后一个chunk将变小。
a = torch.randn(4, 5)
# 沿行分为2块
torch.chunk(a, 2, dim = 0)
(tensor([[-0.5815, 1.3466, 1.8123, -0.2137, 1.0332],
[ 0.2855, -0.2596, -0.1466, 1.8626, -1.5094]]),
tensor([[-0.7512, 0.3702, 0.6753, 2.0602, 0.9390],
[ 1.2926, 0.3564, 0.0266, -1.6133, 0.6239]]))
# 沿列分为3块, 除不尽，最后一块张量只有一列
torch.chunk(a, 3, dim = 1)
(tensor([[-0.5815, 1.3466],
[ 0.2855, -0.2596],
[-0.7512, 0.3702],
[ 1.2926, 0.3564]]),
tensor([[ 1.8123, -0.2137],
[-0.1466, 1.8626],
[ 0.6753, 2.0602],
[ 0.0266, -1.6133]]),
tensor([[ 1.0332],
[-1.5094],
[ 0.9390],
[ 0.6239]]))
3.3 torch.dstack()
torch.dstack(tensors, *, out=None) → Tensor
按顺序(沿第三维)叠加张量
注意：要叠加的tensor前两个维度一定要相等
# 可以叠加多个tensor
a = torch.rand(2, 3, 3)
b = torch.rand(2, 3, 3)
c = torch.rand(2, 3, 3)
d = torch.dstack((a, b, c))
print(d)
print(d.size())
tensor([[[0.6743, 0.6997, 0.5677, 0.9193, 0.4914, 0.4055, 0.0591, 0.0654,
0.2638],
[0.1526, 0.2773, 0.7118, 0.5617, 0.3533, 0.0911, 0.4904, 0.1828,
0.8082],
[0.7754, 0.7911, 0.2999, 0.6562, 0.2948, 0.9407, 0.8955, 0.2537,
0.7910]],
[[0.9034, 0.5289, 0.1748, 0.8016, 0.0661, 0.8339, 0.5822, 0.5961,
0.8841],
[0.3712, 0.3345, 0.5839, 0.5781, 0.1134, 0.0874, 0.4147, 0.2677,
0.4564],
[0.2621, 0.5943, 0.7056, 0.8602, 0.9650, 0.1913, 0.1075, 0.8469,
0.9479]]])
torch.Size([2, 3, 9])
# 如果要叠加的tensor维度小于三维，先使用torch.atleast_3d()转换成3维
a = torch.tensor([[1, 2, 3],[2, 4, 6]])
b = torch.tensor([[4, 5, 6],[7, 8, 9]])
print(a.size())
print(b.size())
c = torch.dstack((a, b))
print(c)
print(c.size())
torch.Size([2, 3])
torch.Size([2, 3])
tensor([[[1, 4],
[2, 5],
[3, 6]],
[[2, 7],
[4, 8],
[6, 9]]])
torch.Size([2, 3, 2])
3.4 torch.gather()
torch.gather(input, dim, index, *, sparse_grad=False, out=None) → Tensor
沿给定轴 dim ,按输入索引张量 index 指定位置的值进行收集组成新的张量。
dim指定哪一个维度
index指定具体索引位置
a = torch.randint(0, 30, (2, 3, 5))
a
tensor([[[ 0, 15, 7, 28, 16],
[12, 22, 23, 4, 4],
[ 6, 3, 14, 22, 13]],
[[23, 4, 20, 10, 4],
[22, 9, 27, 5, 22],
[24, 3, 9, 11, 15]]])
# 三维张量(2, 3, 5)，dim = 1时，index的值可以取 0, 1, 2;
index = torch.LongTensor([[[0, 1, 2, 2, 0],
[0, 0, 0, 0, 0],
[1, 1, 1, 1, 1]],
[[1, 0, 2, 1, 2],
[0, 0, 0, 0, 0],
[2, 2, 2, 2, 2]]])
b = torch.gather(a, 1, index)
b
tensor([[[ 0, 22, 14, 22, 16],
[ 0, 15, 7, 28, 16],
[12, 22, 23, 4, 4]],
[[22, 4, 9, 5, 15],
[23, 4, 20, 10, 4],
[24, 3, 9, 11, 15]]])
我还是老方法理解，三维张量[[[]]]，dim=0是第一层（最外层），dim=1是第二层，dim=3是第三层（最内层）
dim=1时，按第二层[[ 0, 15, 7, 28, 16],[12, 22, 23, 4, 4],[ 6, 3, 14, 22, 13]]
index的[0, 1, 2, 2, 0]就是对应的[ 0, 22, 14, 22, 16]
# 三维张量(2, 3, 5)，dim = 2时，index的值也是可以取 0, 1, 2，3，4;
index = torch.LongTensor([[[0, 2, 1, 3, 4],
[0, 0, 0, 0, 0],
[4, 4, 4, 4, 4]],
[[4, 0, 3, 1, 2],
[0, 0, 0, 0, 0],
[2, 2, 2, 2, 2]]])
c = torch.gather(a, 2, index)
c
tensor([[[ 0, 7, 15, 28, 16],
[12, 12, 12, 12, 12],
[13, 13, 13, 13, 13]],
[[ 4, 23, 10, 4, 20],
[22, 22, 22, 22, 22],
[ 9, 9, 9, 9, 9]]])
dim=2时,按第三层（最内层）[ 0, 15, 7, 28, 16]
index的[0, 2, 1, 3, 4]就是对应的[0, 7, 15, 28, 16]
# 三维张量(2, 3, 5)，dim = 0时，index的值也是可以取 0, 1;
index = torch.LongTensor([[[0, 1, 1, 0, 1],
[0, 0, 0, 0, 0],
[0, 0, 0, 1, 1]],
[[1, 0, 0, 1, 1],
[0, 0, 0, 0, 0],
[1, 1, 1, 1, 1]]])
d = torch.gather(a, 0, index)
d
tensor([[[ 0, 4, 20, 28, 4],
[12, 22, 23, 4, 4],
[ 6, 3, 14, 11, 15]],
[[23, 15, 7, 10, 4],
[12, 22, 23, 4, 4],
[24, 3, 9, 11, 15]]])
dim=0时，按第一层（最外层），即整个张量a, a的上半部分对应的是index中的0，下半部分对应的是index中的1
index中的[0, 1, 1, 0, 1]对应的 [上半部分中的0，下半部分的4，下半部分的20，上半部分的28，下半部分的4]
3.5 torch.hstack()
torch.hstack(tensors, *, out=None) → Tensor
水平方向上（沿第二维）按顺序堆叠张量。
类比3.3torch.dstach()
# 一维时，等价于沿着第一个张量水平拼接第二个张量
a = torch.tensor([1, 2, 3])
b = torch.tensor([4, 5, 6])
c = torch.hstack((a, b))
print(c)
print(c.size())
tensor([1, 2, 3, 4, 5, 6])
torch.Size([6])
# 二维时，沿着第二维水平拼接张量
a = torch.tensor([[1], [2], [3]])
b = torch.tensor([[4], [5], [6]])
print(a.size())
c = torch.hstack((a, b))
print(c)
print(c.size())
torch.Size([3, 1])
tensor([[1, 4],
[2, 5],
[3, 6]])
torch.Size([3, 2])
# 大于二维时，也是沿着第二维水平拼接张量
a = torch.rand(2, 3, 4)
b = torch.rand(2, 3, 4)
print(a)
print(b)
c = torch.hstack((a, b))
print(c)
print(c.size()) # 输出size应该是（2， 6， 4）
tensor([[[0.9993, 0.3971, 0.3522, 0.6960],
[0.2972, 0.5581, 0.3707, 0.9967],
[0.1729, 0.1846, 0.4583, 0.1432]],
[[0.1392, 0.4144, 0.6231, 0.1775],
[0.3527, 0.2495, 0.0369, 0.3696],
[0.0309, 0.1674, 0.9390, 0.6351]]])
tensor([[[0.7380, 0.7130, 0.6342, 0.0902],
[0.6269, 0.4198, 0.2047, 0.4107],
[0.7155, 0.6780, 0.1810, 0.7113]],
[[0.1136, 0.7194, 0.6350, 0.6444],
[0.6555, 0.4313, 0.1446, 0.3732],
[0.7456, 0.0401, 0.3066, 0.2646]]])
tensor([[[0.9993, 0.3971, 0.3522, 0.6960],
[0.2972, 0.5581, 0.3707, 0.9967],
[0.1729, 0.1846, 0.4583, 0.1432],
[0.7380, 0.7130, 0.6342, 0.0902],
[0.6269, 0.4198, 0.2047, 0.4107],
[0.7155, 0.6780, 0.1810, 0.7113]],
[[0.1392, 0.4144, 0.6231, 0.1775],
[0.3527, 0.2495, 0.0369, 0.3696],
[0.0309, 0.1674, 0.9390, 0.6351],
[0.1136, 0.7194, 0.6350, 0.6444],
[0.6555, 0.4313, 0.1446, 0.3732],
[0.7456, 0.0401, 0.3066, 0.2646]]])
torch.Size([2, 6, 4])
3.6 torch.index_select()
torch.index_select(input, dim, index, *, out=None) → Tensor
按照指定维度(dim)和指定行或列(index)从输入张量中重新生成一个张量。
index参数也是一个一维张量，指定具体的行和列
a = torch.rand(3, 4)
print(a)
index = torch.tensor([0, 2]) # 0指定第一行或第一列，2指定第三行或第三列
tensor([[0.5075, 0.1276, 0.5497, 0.7082],
[0.0381, 0.5187, 0.4642, 0.5474],
[0.6278, 0.9411, 0.7611, 0.8547]])
# dim = 0时，按行选取第1行和第三行
torch.index_select(a, 0, index)
tensor([[0.5075, 0.1276, 0.5497, 0.7082],
[0.6278, 0.9411, 0.7611, 0.8547]])
# dim = 1时，按行选取第1列和第三列
torch.index_select(a, 1, index)
tensor([[0.5075, 0.5497],
[0.0381, 0.4642],
[0.6278, 0.7611]])
3.7 torch.masked_select()
torch.masked_select(input, mask, *, out=None) → Tensor
根据布尔掩码(布尔掩码是BoolTensor)对输入张量进行索引返回一个一维新张量。
# 对应bool值是True留下，对应bool值是False舍弃
x = torch.randn(3, 4)
print(x)
mask = x.ge(0.5) # torch.ge()和x中的每个值比较，大于0.5的返回True,小于的返回False
print(mask)
torch.masked_select(x, mask)
tensor([[ 0.1202, 1.0083, 1.1095, 0.7824],
[-0.0164, -1.8309, 1.1025, -0.2821],
[ 0.2242, -2.3140, 1.6496, -0.5039]])
tensor([[False, True, True, True],
[False, False, True, False],
[False, False, True, False]])
tensor([1.0083, 1.1095, 0.7824, 1.1025, 1.6496])
3.8 torch.movedim()
torch.movedim(input, source, destination) → Tensor
在input多维张量中，指定原始维数和目标维数进行交换返回一个新张量。
可同时交换多个维数
source参数指定原张量交换维数，destination参数指定目标交换维数
# 交换一个维数
t = torch.randn(3, 2, 1)
print(t)
s = torch.movedim(t, 1, 0).shape
print(s)
torch.movedim(t, 1, 0)
tensor([[[ 0.4433],
[-0.5050]],
[[ 0.4186],
[-1.3052]],
[[-0.1917],
[ 0.1454]]])
torch.Size([2, 3, 1])
tensor([[[ 0.4433],
[ 0.4186],
[-0.1917]],
[[-0.5050],
[-1.3052],
[ 0.1454]]])
# 交换多个维数
s = torch.movedim(t, (1, 2), (0, 1)).shape
print(s)
torch.movedim(t, (1, 2), (0, 1))
torch.Size([2, 1, 3])
tensor([[[ 0.4433, 0.4186, -0.1917]],
[[-0.5050, -1.3052, 0.1454]]])
3.9 torch.narrow()
torch.narrow(input, dim, start, length) → Tensor
从输入张量中缩小张量，返回一个新张量
dim参数指定维度，0指行，1指列
start开始的行或列，length指定长度，共几行或几列
# (x, 0, 0, 2) 按行从第一行开始到第二行结束缩小x张量
x = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
torch.narrow(x, 0, 0, 2)
tensor([[1, 2, 3],
[4, 5, 6]])
# (x, 1, 1, 2) 按列从第一列开始到第二列结束缩小x张量
torch.narrow(x, 1, 1, 2)
tensor([[2, 3],
[5, 6],
[8, 9]])
3.10 torch.nonzero()
torch.nonzero(input, *, out=None, as_tuple=False) → LongTensor or tuple of LongTensors
返回输入张量中非零元素的索引
# 0， 1， 2， 4是非零元素
torch.nonzero(torch.tensor([1, 1, 1, 0, 1]))
tensor([[0],
[1],
[2],
[4]])
# 多维时，返回[行，列]的索引形式
# [0, 0]指非零元素0.6
torch.nonzero(torch.tensor([[0.6, 0.0, 0.0, 0.0],
[0.0, 0.4, 0.0, 0.0],
[0.0, 0.0, 1.2, 0.0],
[0.0, 0.0, 0.0, -0.4]]))
tensor([[0, 0],
[1, 1],
[2, 2],
[3, 3]])
# 参数as_tuple默认是False, 如果设置为True，多维时就是分别返回行的索引和列的索引
# 返回的[0, 1, 2, 3]时非零元素的行索引，[0, 1, 3, 3]是非零元素的列索引
torch.nonzero(torch.tensor([[0.6, 0.0, 0.0, 0.0],
[0.0, 0.4, 0.0, 0.0],
[0.0, 0.0, 0.0, 1.0],
[0.0, 0.0, 0.0,-0.4]]), as_tuple=True)
(tensor([0, 1, 2, 3]), tensor([0, 1, 3, 3]))
3.11 torch.reshape()
torch.reshape(input, shape) → Tensor
这个还是经常用的，返回一个张量，该张量具有相同的数据和作为输入的元素数量，但具有指定的形状。
a = torch.rand(3, 4)
print(a)
tensor([[0.2954, 0.2388, 0.2867, 0.2606],
[0.0425, 0.2325, 0.8777, 0.1897],
[0.0761, 0.9029, 0.9126, 0.0192]])
b = torch.reshape(a, (4, 3))
print(b)
tensor([[0.2954, 0.2388, 0.2867],
[0.2606, 0.0425, 0.2325],
[0.8777, 0.1897, 0.0761],
[0.9029, 0.9126, 0.0192]])
# 参数设置-1，时，返回一维张量
c = torch.tensor([[[0, 1], [2, 3]],
[[4, 5], [6, 7]]])
torch.reshape(c, (-1,))
tensor([0, 1, 2, 3, 4, 5, 6, 7])
3.12 torch.split()
torch.split(tensor, split_size_or_sections, dim=0)
把张量分成块。每一块都是原始张量的一个视图。
a = torch.arange(10).reshape(5, 2)
a
tensor([[0, 1],
[2, 3],
[4, 5],
[6, 7],
[8, 9]])
# int 分块
torch.split(a, 2)
(tensor([[0, 1],
[2, 3]]),
tensor([[4, 5],
[6, 7]]),
tensor([[8, 9]]))
# list 分块
torch.split(a, (1, 4))
(tensor([[0, 1]]),
tensor([[2, 3],
[4, 5],
[6, 7],
[8, 9]]))
3.13 torch.squeeze()
torch.squeeze(input, dim=None, *, out=None) → Tensor
默认返回一个去掉所有维数为1的维度后的张量；如果指定参数dim，可以去除指定的维数为1的维度
对应的函数是torch.unsqueeze()可实现给指定位置增加维数为1的维度
x = torch.zeros(2, 1, 2, 1, 2)
x.size()
torch.Size([2, 1, 2, 1, 2])
# 不指定dim
y = torch.squeeze(x)
y.size()
torch.Size([2, 2, 2])
# 指定dim,如果指定的不是维数为1，去除无效
y = torch.squeeze(x, 1)
y.size()
torch.Size([2, 2, 1, 2])
3.14 torch.stack()
torch.stack(tensors, dim=0, *, out=None) → Tensor
沿一个新维度对输入张量序列进行连接，序列中所有张量应为相同形状；
stack 函数返回的结果会新增一个维度，而stack（）函数指定的dim参数，就是新增维度的（下标）位置。
a = torch.rand(1, 2, 3)
print(a, a.size())
b = torch.rand(1, 2, 3)
print(b, b.size())
c = torch.stack([a, b])
print(c, c.size())
# 可以在指定维度进行拼接
d = torch.stack([a, b], 1)
print(d, d.size())
tensor([[[0.8866, 0.2823, 0.1084],
[0.7682, 0.4529, 0.2383]]]) torch.Size([1, 2, 3])
tensor([[[0.4213, 0.3009, 0.1707],
[0.1252, 0.5748, 0.4927]]]) torch.Size([1, 2, 3])
tensor([[[[0.8866, 0.2823, 0.1084],
[0.7682, 0.4529, 0.2383]]],
[[[0.4213, 0.3009, 0.1707],
[0.1252, 0.5748, 0.4927]]]]) torch.Size([2, 1, 2, 3])
tensor([[[[0.8866, 0.2823, 0.1084],
[0.7682, 0.4529, 0.2383]],
[[0.4213, 0.3009, 0.1707],
[0.1252, 0.5748, 0.4927]]]]) torch.Size([1, 2, 2, 3])
3.15 torch.t()
torch.t(input) → Tensor
简单来说就是对输入张量进行转置
如果输入的0维或1维张量，保持原样输出
a = torch.tensor([1, 2, 3])
torch.t(a)
tensor([1, 2, 3])
b = torch.rand(2, 3)
print(b, b.size())
c = torch.t(b)
print(c, c.size())
tensor([[0.6441, 0.1929, 0.8621],
[0.8585, 0.8823, 0.6143]]) torch.Size([2, 3])
tensor([[0.6441, 0.8585],
[0.1929, 0.8823],
[0.8621, 0.6143]]) torch.Size([3, 2])
3.16 torch.take()
torch.take(input, index) → Tensor
输入张量被看作是一个一维张量，按给定index索引取值，返回一个新的张量。
a = torch.tensor([[1, 2, 3],
[4, 5, 6]])
index = torch.tensor([0, 2, 5])
# 取出第1，3，6的数重新组成一个张量
torch.take(a, index)
tensor([1, 3, 6])
3.17 torch.transpose()
torch.transpose(input, dim0, dim1) → Tensor
对输入张量进行转置
注意：得到的张量与输入张量共享它的底层存储，所以改变一个张量的内容就会改变另一个张量的内容。
x = torch.rand(2, 3)
print(x, x.size())
y = torch.transpose(x, 0, 1)
print(y, y.size())
tensor([[0.9390, 0.0851, 0.1133],
[0.0745, 0.1473, 0.1422]]) torch.Size([2, 3])
tensor([[0.9390, 0.0745],
[0.0851, 0.1473],
[0.1133, 0.1422]]) torch.Size([3, 2])
3.18 torch.unbind()
torch.unbind(input, dim=0) → seq
移除张量的维数，返回原来张量元素的元组
torch.unbind(torch.tensor([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]]))
(tensor([1, 2, 3]), tensor([4, 5, 6]), tensor([7, 8, 9]))
3.19 torch.unsqueeze()
torch.unsqueeze(input, dim) → Tensor
与3.13torch.squeeze()对比，可实现给指定位置增加维数为1的维度
x = torch.tensor([1, 2, 3, 4, 5])
torch.unsqueeze(x, 0)
tensor([[1, 2, 3, 4, 5]])
torch.unsqueeze(x, 1)
tensor([[1],
[2],
[3],
[4],
[5]])
3.20 torch.vstack()
torch.vstack(tensors, *, out=None) → Tensor
按顺序(按行)堆叠张量。
a = torch.tensor([1, 2, 3])
b = torch.tensor([4, 5, 6])
torch.vstack((a, b))
tensor([[1, 2, 3],
[4, 5, 6]])
a = torch.tensor([[1], [2], [3]])
b = torch.tensor([[4], [5], [6]])
torch.vstack((a, b))
tensor([[1],
[2],
[3],
[4],
[5],
[6]])
3.21 torch.where()
torch.where(condition, x, y) → Tensor
根据条件返回从x或y中选择的元素张量。
condition参数是判断条件
x是符合条件的设置值
y是不符合条件的设置值
x = torch.randn(2, 2, dtype = torch.double)
x
tensor([[-0.2385, -0.0491],
[ 1.5753, 0.0219]], dtype=torch.float64)
# 张量中不满足大于0的设置成0
torch.where(x > 0, x, 0.)
tensor([[0.0000, 0.0000],
[1.5753, 0.0219]], dtype=torch.float64)
reference:
- https://pytorch.org/docs/master/torch.html