pytorch文档阅读，一

本章主要针对pytorch0.4.0英文文档的前两节,顺序可能有些不一样：

• torch
• torch.Tensor

张量 Tensors

• torch.is_stensor/torch.is_storage
• torch.set_default_tensor_type()

  这个有用，如果大部分操作是GPU上构建的，可你把默认类型定为cuda tensor

if torch.cuda.is_available():
    if args.cuda:
        torch.set_default_tensor_type('torch.cuda.FloatTensor')
    if not args.cuda:
        print("WARNING: It looks like you have a CUDA device, but aren't " +
              "using CUDA.\nRun with --cuda for optimal training speed.")
        torch.set_default_tensor_type('torch.FloatTensor')
else:
    torch.set_default_tensor_type('torch.FloatTensor')

• torch.numel(input)->int/numel() /nelement()
• torch.set_printoptions(precision=None, threshold=None, edgeitems=None, linewidth=None, profile=None) 打印选项
• torch.set_flush_denormal(mode) → bool 禁用cpu非常规浮点

>>> torch.set_flush_denormal(True)
True
>>> torch.tensor([1e-323], dtype=torch.float64)
tensor([ 0.], dtype=torch.float64)
>>> torch.set_flush_denormal(False)
True
>>> torch.tensor([1e-323], dtype=torch.float64)
tensor(9.88131e-324 *
       [ 1.0000], dtype=torch.float64)

## 创建操作 Creation Ops

torch方法后缀_like :创建除了值以外的任何设置相同的tensor

包括：zeros，ones，empty， full，rand，randint，randn

• torch.tensor(data, dtype=None, device=None, requires_grad=False) → Tensor
• torch.from_numpy(ndarray) → Tensor
• torch.eye(n, m=None, out=None)
• torch.linspace(start, end, steps=100, out=None) → Tensor
• torch.logspace(start, end, steps=100, out=None) → Tensor
• torch.ones(*sizes, out=None) → Tensor
• torch.empty(*sizes, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor
• torch.reshape(input, shape) → Tensor 注意这是个坑，
• torch.rand(*sizes, out=None) → Tensor（均匀分布）
• torch.randn(*sizes, out=None) → Tensor（正态分布）
• torch.randperm(n, out=None) → LongTensor（随机整数0，，，n-1）
• torch.randint(low=0, high, size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor
• torch.arange(start, end, step=1, out=None) → Tensor/torch.range(start, end, step=1, out=None) → Tensor 一个包含末尾，一个不包含
• torch.zeros(*sizes, out=None) → Tensor

## 索引,切片,连接,编译操作

这些操作绝大多数tensor本身也包含

tensor方法的通用后缀 _ inplace操作，

• torch.cat(seq, dim=0, out=None) → Tensor/torch.stack(sequence, dim=0) 常用操作,一个是存在的维度上，一个是新的维度上（新建一个维度，已经存在的维度自然向后挤了）
• torch.split(tensor, split_size, dim=0)/torch.chunk(tensor, chunks, dim=0)/split()/chunk(),这两个功能相近，一个是沿轴均分指定大小（如果无法整除，最后一块返回较小的块），另一个chunk是返回固定块数（也是和split一样，最后一块返回较小块）

a = torch.Tensor([1,2,3,4,5])
b = a.split(2)
c = a.chunk(3)

• torch.gather(input, dim, index, out=None) → Tensor/gather(dim, index) 这个函数就很迷了，当时学习tensorflow时就研究了好久╮(╯﹏╰）╭，注意所有的index都是torch.LongTensor

 torch.gather(t, 1, torch.LongTensor([[0,0],[1,0]]))

• torch.index_select(input, dim, index, out=None) → Tensor/index_select(dim, index) f非常重要的函数
• torch.masked_select(input, mask, out=None) → Tensor/masked_select(mask) 注意所有mask都为torch.ByteTensor,同时需要注意mask的shape不一定要和tensor相同数量也不一定要相同，shape中必须有一个轴要和tensor的轴对应，此时按此轴索引

a = torch.Tensor([[1,2,3],[4,5,6]])
mask = torch.Tensor([[1,0],[0,0],[1,0]]).type(torch.ByteTensor)
mask_1 = torch.Tensor([[1],[0]]).type(torch.ByteTensor)
mask_2 = torch.Tensor([0,1,1]).type(torch.ByteTensor)
b = a.masked_select(mask)#error
c = a.masked_select(mask_1)
d = a.masked_select(mask_2)

• torch.nonzero(input, out=None) → LongTensor 注意是返回的高维索引

>>> torch.nonzero(torch.Tensor([[0.6, 0.0, 0.0, 0.0],
...                             [0.0, 0.4, 0.0, 0.0],
...                             [0.0, 0.0, 1.2, 0.0],
...                             [0.0, 0.0, 0.0,-0.4]]))

 0  0
 1  1
 2  2
 3  3
[torch.LongTensor of size 4x2]

• torch.squeeze(input, dim=None, out=None)/squeeze(dim=None) 超超超重要的函数
• torch.stack(sequence, dim=0)
• torch.t/t()
• torch.transpose(input, dim0, dim1, out=None) → Tensor/transpose() 交换任意两个维度
• torch.take(input, indices) → Tensor 展开之后的tensor取索引
• tensor.permute(dims) 非常重要

x.permute(2, 0, 1)

• torch.unbind(tensor, dim=0) 移除指定维度，返回一个truple，包含了沿着指定维切片后的各个切片
• torch.unsqueeze(input, dim, out=None) 插入维度
• torch.where(condition, x, y) → Tensor 注意condition (ByteTensor)

随机抽样 Random sampling

• torch.manual_seed(seed)
• torch.initial_seed() 注意做对比实验的时为了控制变量，多线程载入数据时每个线程的seed都需要严格设定
• torch.get_rng_state() ->(ByteTensor)
• torch.set_rng_state
• torch.default_generator
• torch.bernoulli(input, out=None) → Tensor伯努利投硬币，常用于样本的挖掘（hard example）
• torch.multinomial(input, num_samples,replacement=False, out=None) → Longtensor 多项分布抽取样本
• torch.normal(means, std, out=None) 离散正态分布中抽取随机数

torch.normal(means=torch.arange(1, 11), std=torch.arange(1, 0, -0.1))

 1.5104
 1.6955
 2.4895
 4.9185
 4.9895
 6.9155
 7.3683
 8.1836
 8.7164
 9.8916
[torch.FloatTensor of size 10]
>>> torch.normal(mean=0.5, std=torch.arange(1, 6))

  0.5723
  0.0871
 -0.3783
 -2.5689
 10.7893
[torch.FloatTensor of size 5]
>>> torch.normal(means=torch.arange(1, 6))

 1.1681
 2.8884
 3.7718
 2.5616
 4.2500
[torch.FloatTensor of size 5]

序列化 Serialization

• torch.saves
• torch.load

并行化 Parallelism

• torch.get_num_threads
• torch.set_num_threads(int)

数学操作Math operations

tensor有全部的对应数学函数

挑几个常用的：

• ceil/floor/frac
• round
• torch.clamp(input, min, max, out=None) → Tensor 等价于tensorflow的tf.clip
• torch.argmax(input, dim=None, keepdim=False)/torch.argmin(input, dim=None, keepdim=False)
• torch.cumprod(input, dim, out=None) → Tensor $$y_i = x_1 \times x_2\times x_3\times \dots \times x_i$$/torch.prod(input, dim, keepdim=False, out=None) → Tensor
• torch.cumsum(input, dim, out=None) → Tensor 同上
• torch.dist(input, other, p=2) → Tensor p范数/torch.norm(input, p, dim, keepdim=False, out=None) → Tensor
• • torch.mean(input, dim, keepdim=False, out=None) → Tensor 注意keep_dim是是否保持维度不变

>>> a = torch.randn(4, 4)
>>> a
tensor([[-0.3841,  0.6320,  0.4254, -0.7384],
        [-0.9644,  1.0131, -0.6549, -1.4279],
        [-0.2951, -1.3350, -0.7694,  0.5600],
        [ 1.0842, -0.9580,  0.3623,  0.2343]])
>>> torch.mean(a, 1)
tensor([-0.0163, -0.5085, -0.4599,  0.1807])
>>> torch.mean(a, 1, True)
tensor([[-0.0163],
        [-0.5085],
        [-0.4599],
        [ 0.1807]])

• torch.median() 返回中间值

>>> a = torch.randn(1, 3)
>>> a
tensor([[ 1.5219, -1.5212,  0.2202]])
>>> torch.median(a)
tensor(0.2202)

• torch.median(input, dim=-1, keepdim=False, values=None, indices=None) -> (Tensor, LongTensor)

>>> a = torch.randn(4, 5)
>>> a
tensor([[ 0.2505, -0.3982, -0.9948,  0.3518, -1.3131],
        [ 0.3180, -0.6993,  1.0436,  0.0438,  0.2270],
        [-0.2751,  0.7303,  0.2192,  0.3321,  0.2488],
        [ 1.0778, -1.9510,  0.7048,  0.4742, -0.7125]])
>>> torch.median(a, 1)
(tensor([-0.3982,  0.2270,  0.2488,  0.4742]), tensor([ 1,  4,  4,  3]))

• torch.std(input, dim, keepdim=False, unbiased=True, out=None) → Tensor/torch.var(input, dim, keepdim=False, unbiased=True, out=None) → Tensor 标准差和方差
• torch.sum(input, dim, keepdim=False, out=None) → Tensor
• torch.unique(input, sorted=False, return_inverse=False) 元素去重（只能是1D tensor）

Comparison Ops

• torch.eq(input, other, out=None) → Tensor other必须能广播，返回mask
• torch.equal(tensor1, tensor2) → bool
• torch.ge/gt/le/lt/ne/ ≥/＞/≤/＜
• torch.topk(input, k, dim=None, largest=True, sorted=True, out=None) -> (Tensor, LongTensor)/torch.kthvalue(input, k, dim=None, keepdim=False, out=None) -> (Tensor, LongTensor)
• torch.max(input) → Tensor
• torch.max(input, dim, keepdim=False, out=None) -> (Tensor, LongTensor)
• torch.max(input, other, out=None) → Tensor

>>> a = torch.randn(4)
>>> a
tensor([ 0.2942, -0.7416,  0.2653, -0.1584])
>>> b = torch.randn(4)
>>> b
tensor([ 0.8722, -1.7421, -0.4141, -0.5055])
>>> torch.max(a, b)
tensor([ 0.8722, -0.7416,  0.2653, -0.1584])

• torch.min()也有三种方法，使用同max
• torch.sort(input, dim=None, descending=False, out=None) -> (Tensor, LongTensor)

BLAS and LAPACK Operations

各种矩阵的基础运算

Spectral Ops

终于算是加上了。。

Tensor独有的Ops

tensor的前缀new_方法,是固定变量赋值，适用于ones，zeros，full，tensor（坑）

>>> tensor = torch.ones((2,), dtype=torch.int8)
>>> data = [[0, 1], [2, 3]]
>>> tensor.new_tensor(data)
tensor([[ 0,  1],[ 2,  3]], dtype=torch.int8)

• torch.Tensor.item() 坑，注意只能是一个值，适合返回loss，acc等
• apply_(callable) → Tensor(类似于map，python层面的cpu funtion，效率低)
• cauchy_(median=0, sigma=1, *, generator=None) → Tensor
• char(),byte(),double() ,int()
• clone() /copy() 第一个是完全克隆，第二个是可广播的数值
• contiguous() → Tensor 一些op为了高效运算，默认实现连续内存运算需求的，这时候要保证tensor的连续存储
• is_contiguous() → bool/is_pinned()/is_cuda/is_pinned()/is_signed()
• cpu()/cuda()
• dim()
• device
• element_size() → int 返回变量类型的内存占用字节
• expand(*sizes) → Tensor 重要：扩展dim维1的轴

>>> x = torch.tensor([[1], [2], [3]])
>>> x.size()
torch.Size([3, 1])
>>> x.expand(3, 4)
tensor([[ 1,  1,  1,  1],
        [ 2,  2,  2,  2],
        [ 3,  3,  3,  3]])
>>> x.expand(-1, 4)   # -1 means not changing the size of that dimension
tensor([[ 1,  1,  1,  1],
        [ 2,  2,  2,  2],
        [ 3,  3,  3,  3]])

• index_copy_(dim, index, tensor) → Tensor 按索引复制元素

>>> x = torch.zeros(5, 3)
>>> t = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=torch.float)
>>> index = torch.tensor([0, 4, 2])
>>> x.index_copy_(0, index, t)
tensor([[ 1.,  2.,  3.],
        [ 0.,  0.,  0.],
        [ 7.,  8.,  9.],
        [ 0.,  0.,  0.],
        [ 4.,  5.,  6.]])

• index_fill_(dim, index, val) → Tensor
• map_(tensor, callable)

  Applies callable for each element in self tensor and the given tensor and stores the results in self tensor. self tensor and the given tensor must be broadcastable.

  (待续)

一些坑

• new_tensor 会新建变量，use torch.Tensor.requires_grad_() or torch.Tensor.detach()
• mask_select index_select也会新建变量
• reshape，resize，review

  （待续）