《Pytorch学习指南》- 基础API(1)

文章目录

- - 1. PytorchTensor base
  - - 1.1 torch.tensor
    - 1.2 torch.Tensor
    - 1.3 torch.empty
    - 1.4 torch.zeros
    - 1.5 torch.zeros_like
    - 1.6 torch.eye
    - 1.7 torch.ones
    - 1.8 torch.ones_like
    - 1.9 torch.rand
    - 1.10 torch.arange
    - 1.11 torch.linspace
    - 1.12 torch.normal
    - 1.13 tensor.uniform_
    - 1.14 torch.randperm
  - 2. Tensor Property
  - - 2.1 稀疏张量的表示
    - 2.2 稀疏矩阵转稠密矩阵
  - 3. Tensor operator
  - - 3.1 加法运算
    - 3.2 减法运算
    - 3.3 乘法运算
    - 3.4 除法运算
    - 3.5 矩阵运算
    - 3.6 高维tensor计算
    - 3.7 torch.pow
    - 3.8 torch.exp
    - 3.9 torch.log
    - 3.10 torch.sqrt

1. PytorchTensor base 1.1 torch.tensor

import torch

for module in torch,:
    print(module.__name__, module.__version__)

torch 1.9.1+cpu

a = torch.tensor([[1, 2, 3], [4, 5, 6]])
print(a)
print(a.type())

tensor([[1, 2, 3],
        [4, 5, 6]])
torch.LongTensor

1.2 torch.Tensor

# 不指定数据, 会随机生成数据
b = torch.Tensor(2, 2)
print(b)

tensor([[4.5001e-39, 4.7755e-39],
        [5.1429e-39, 5.9694e-39]])

# 使用tuple创建
d = torch.tensor(((1, 2), (3, 4)))
print(d.type())
print(d.type_as(a))

torch.LongTensor
tensor([[1, 2],
        [3, 4]])

1.3 torch.empty

d = torch.empty(2, 3)
print(d)
print(d.type())
print(d.type_as(a))

tensor([[2.1762e-04, 1.0529e-11, 1.3721e-05],
        [6.5558e-10, 2.7594e-06, 3.1948e+21]])
torch.FloatTensor
tensor([[                   0,                    0,                    0],
        [                   0,                    0, -9223372036854775808]])

1.4 torch.zeros

a = torch.tensor([[1, 2, 3], [4, 5, 6]])
d = torch.zeros(2, 3)
print(d.type())
# 将 d 的类型转换为 tensor a的类型
print(d.type_as(a).type())

torch.FloatTensor
torch.LongTensor

1.5 torch.zeros_like

将已有的tensor变成全0

d = torch.Tensor(2, 3)
print(d)
d = torch.zeros_like(d)
print(d)
print(d.type())

tensor([[0.0000e+00, 0.0000e+00, 1.7661e-04],
        [2.1254e-07, 5.3736e-05, 3.3243e+21]])
tensor([[0., 0., 0.],
        [0., 0., 0.]])
torch.FloatTensor

1.6 torch.eye

生成对角相同的数据

d = torch.eye(2, 2)
print(d.type())
print(d.type_as(a))

torch.FloatTensor
tensor([[1, 0],
        [0, 1]])

1.7 torch.ones

生成全是1的tensor

d = torch.ones(2, 2)
print(d.type())
print(d.type_as(a))

torch.FloatTensor
tensor([[1, 1],
        [1, 1]])

1.8 torch.ones_like

将已有的数据转换为全是1的tensor

d = torch.tensor([1, 2, 3])
d = torch.ones_like(d)
print(d.type())
print(d.type_as(a))

torch.LongTensor
tensor([1, 1, 1])

1.9 torch.rand

生成指定shape的随机数(0~1)

d = torch.rand(2, 3)
print(d.type())
print(d)

torch.FloatTensor
tensor([[0.5115, 0.9366, 0.6257],
        [0.7940, 0.5914, 0.6784]])

1.10 torch.arange

生成指定范围(步长)的数据
arange(start, end, step)

d = torch.arange(2, 10, 2)
print(d.type())
print(d.type_as(a))

torch.LongTensor
tensor([2, 4, 6, 8])

1.11 torch.linspace

生成等间隔的数据
linspace(start, end, step)

d = torch.linspace(10, 2, 3)
print(d.type())
print(d.type_as(a))

torch.FloatTensor
tensor([10,  6,  2])

1.12 torch.normal

生成正态分布的数据
normal(mean=均值, std=方差, size=(2, 3), out=输出到指定tensor)

dd = torch.normal(mean=0, std=1, size=(2, 3), out=b)
print(b)
print(dd)

tensor([[ 0.2942, -0.3772,  0.7297],
        [-0.5637, -0.6795,  1.2599]])
tensor([[ 0.2942, -0.3772,  0.7297],
        [-0.5637, -0.6795,  1.2599]])

d = torch.normal(mean=torch.rand(5), std=torch.rand(5))
print(d.type())
print(d)

torch.FloatTensor
tensor([ 0.3071,  0.7098,  1.1409, -0.0179,  0.0629])

1.13 tensor.uniform_

生成均匀分布的数据
uniform_(-1, 1) : 生成-1 到 1 之间均匀分布的数据

d = torch.Tensor(2, 2)
print(d)
print(d.uniform_(-1, 1))
print(d.type())

tensor([[1.5413e-01,        nan],
        [8.4078e-45, 0.0000e+00]])
tensor([[-0.3093,  0.1627],
        [ 0.3768, -0.6339]])
torch.FloatTensor

1.14 torch.randperm

随机打乱指定的数据

d = torch.randperm(10)
print(d.type())
print(d.type_as(a))

torch.LongTensor
tensor([6, 3, 4, 2, 0, 5, 9, 1, 8, 7])

2. Tensor Property

每一个Tensor有torch.dtype、torch.device、torch.layout三种属性。
1. device : 表示GPU的设备, 通常用 conda:0, conda1, … 表示
2. torch.device(“cpu”) 定义将 tensor 放到cpu中
torch.device标识了torch.Tensor对象在创建之后所存储在的设备名称
torch.layout表示torch.Tensor内存布局的对象。

2.1 稀疏张量的表示

torch.sparse_coo_tensor
1. indices : 坐标 => [[1,2,3], [0,0,0]] = (1,0) (2,0) (3,0)
2. size : shape
3. values : 稀疏矩阵值

import torch

# 定义将 tensor 放到cpu中
dev = torch.device("cpu")
# 定义将 tensor 放到Gpu中
# dev = torch.device("cuda")

将tensor放到cpu中

a = torch.tensor([2, 2],
                 dtype=torch.float32,
                 device=dev)
print(a)

tensor([2., 2.])

i = torch.tensor([[0, 1, 2, 3], [0, 1, 2, 3]])
v = torch.tensor([1, 2, 3, 4])
a = torch.sparse_coo_tensor(indices=i, values=v, size=(4, 4),
                            dtype=torch.float32,
                            device=dev)
print(a)

tensor(indices=tensor([[0, 1, 2, 3],
                       [0, 1, 2, 3]]),
       values=tensor([1., 2., 3., 4.]),
       size=(4, 4), nnz=4, layout=torch.sparse_coo)

2.2 稀疏矩阵转稠密矩阵

i = torch.tensor([[0, 1, 2], [0, 1, 2]])
v = torch.tensor([1, 2, 3])
a = torch.sparse_coo_tensor(i, v, (4, 4),
                            dtype=torch.float32,
                            device=dev)
print(a.type())
print(a)
# 转稠密矩阵
print(a.to_dense())

torch.sparse.FloatTensor
tensor(indices=tensor([[0, 1, 2],
                       [0, 1, 2]]),
       values=tensor([1., 2., 3.]),
       size=(4, 4), nnz=3, layout=torch.sparse_coo)
tensor([[1., 0., 0., 0.],
        [0., 2., 0., 0.],
        [0., 0., 3., 0.],
        [0., 0., 0., 0.]])

3. Tensor operator

本章节介绍Tensor的基本运算

3.1 加法运算

加法运算直接返回结果
- a + b
- a.add(b)
- torch.add(a, b)
修改原先的值
- a.add_(b) : 会将a+b的结果直接赋值给a

a = torch.tensor([1,2,3])
b = torch.tensor([4,5,6])
print(a)
print(b)

tensor([1, 2, 3])
tensor([4, 5, 6])

print(a + b)
print(a.add(b))
print(torch.add(a, b))
print(a)
# 会修改a的值
print(a.add_(b))
print(a)

tensor([5, 7, 9])
tensor([5, 7, 9])
tensor([5, 7, 9])
tensor([1, 2, 3])
tensor([5, 7, 9])
tensor([5, 7, 9])

3.2 减法运算

直接返回结果
- a - b
- torch.sub(a, b)
- a.sub(b)
将返回结果赋值给变量
- a.sub_(b) 将计算的结果赋值给a

a = torch.tensor([4,5,6])
b = torch.tensor([1,2,3])

print(a - b)
print(torch.sub(a, b))
print(a.sub(b))
print(a.sub_(b))
print(a)

tensor([3, 3, 3])
tensor([3, 3, 3])
tensor([3, 3, 3])
tensor([3, 3, 3])
tensor([3, 3, 3])

3.3 乘法运算

注意 : 对应的值乘以对应的值, 并不是矩阵运算
直接返回结果
1. a * b
2. torch.mul(a, b)
3. a.mul(b)
将返回的值赋给变量
1. a.mul_(b) 将结果直接赋值给a

a = torch.tensor([[1,1,1], [2,2,2], [3,3,3]])
b = torch.tensor([[1,1,1], [2,2,2], [3,3,3]])

print(a * b)
print(torch.mul(a, b))
print(a.mul(b))
print(a)
print(a.mul_(b))
print(a)

tensor([[1, 1, 1],
        [4, 4, 4],
        [9, 9, 9]])
tensor([[1, 1, 1],
        [4, 4, 4],
        [9, 9, 9]])
tensor([[1, 1, 1],
        [4, 4, 4],
        [9, 9, 9]])
tensor([[1, 1, 1],
        [2, 2, 2],
        [3, 3, 3]])
tensor([[1, 1, 1],
        [4, 4, 4],
        [9, 9, 9]])
tensor([[1, 1, 1],
        [4, 4, 4],
        [9, 9, 9]])

3.4 除法运算

直接返回结果
1. a/b
2. torch.div(a, b)
3. a.div(b)
将返回结果赋值给指定变量
1. a.div_(b)

a = torch.tensor([[1,1,1], [2,2,2], [3,3,3]], dtype=torch.float32)
b = torch.tensor([[2,2,2], [2,2,2], [2,2,2]], dtype=torch.float32)

print(a/b)
print(torch.div(a, b))
print(a.div(b))
print(a.div_(b))
print(a)

tensor([[0.5000, 0.5000, 0.5000],
        [1.0000, 1.0000, 1.0000],
        [1.5000, 1.5000, 1.5000]])
tensor([[0.5000, 0.5000, 0.5000],
        [1.0000, 1.0000, 1.0000],
        [1.5000, 1.5000, 1.5000]])
tensor([[0.5000, 0.5000, 0.5000],
        [1.0000, 1.0000, 1.0000],
        [1.5000, 1.5000, 1.5000]])
tensor([[0.5000, 0.5000, 0.5000],
        [1.0000, 1.0000, 1.0000],
        [1.5000, 1.5000, 1.5000]])
tensor([[0.5000, 0.5000, 0.5000],
        [1.0000, 1.0000, 1.0000],
        [1.5000, 1.5000, 1.5000]])

3.5 矩阵运算

返回计算结果
1. a @ b
2. a.matmul(b)
3. torch.matmul(a, b)
4. torch.mm(a, b)
5. a.mm(b)

a = torch.tensor([[1,1], [2,2]])
b = torch.tensor([[1,1], [2,2]])

# [1,1] * [1,2] = 3, [2,2] * [1,2] = 6
print(a @ b)
print(a.matmul(b))
print(torch.matmul(a, b))
print(torch.mm(a, b))
print(a.mm(b))

tensor([[3, 3],
        [6, 6]])
tensor([[3, 3],
        [6, 6]])
tensor([[3, 3],
        [6, 6]])
tensor([[3, 3],
        [6, 6]])
tensor([[3, 3],
        [6, 6]])

3.6 高维tensor计算

# 四维向量
a = torch.ones(1, 2, 3, 4)
b = torch.ones(1, 2, 4, 3)
print(a.matmul(b).shape)

torch.Size([1, 2, 3, 3])

3.7 torch.pow

pow(var, n) 计算var的n次方
直接返回结果
1. torch.pow(a, 3)
2. a.pow(3)
3. a**3
a.pow_(3)
1. 直接返回结果, 然后将结果赋值给变量a

a = torch.tensor([1, 2])
print(torch.pow(a, 3))
print(a.pow(3))
print(a**3)
print(a.pow_(3))
print(a)

tensor([1, 8])
tensor([1, 8])
tensor([1, 8])
tensor([1, 8])
tensor([1, 8])

3.8 torch.exp

a = torch.tensor([1, 2],
                 dtype=torch.float32)
print(a.type())
print(torch.exp(a))
print(torch.exp_(a))
print(a.exp())
print(a.exp_())

torch.FloatTensor
tensor([2.7183, 7.3891])
tensor([2.7183, 7.3891])
tensor([  15.1543, 1618.1781])
tensor([  15.1543, 1618.1781])

3.9 torch.log

a = torch.tensor([10, 2],
                 dtype=torch.float32)
print(torch.log(a))
print(torch.log_(a))
print(a.log())
print(a.log_())

tensor([2.3026, 0.6931])
tensor([2.3026, 0.6931])
tensor([ 0.8340, -0.3665])
tensor([ 0.8340, -0.3665])

3.10 torch.sqrt

a = torch.tensor([10, 2],
                 dtype=torch.float32)
print(torch.sqrt(a))
print(torch.sqrt_(a))

print(a.sqrt())
print(a.sqrt_())

tensor([3.1623, 1.4142])
tensor([3.1623, 1.4142])
tensor([1.7783, 1.1892])
tensor([1.7783, 1.1892])

《Pytorch学习指南》- 基础API(1)

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