torch从零学习笔记系列

Tensor

tensor 是torch里最重要的一种数据结构，专门为数值数据设计的

Multi-dimensional matrix

一个tensor 可以认为是一个多维矩阵，我们处理数据的时候经常把数据存储在一个四维矩阵中[N,C,H,W] batchsize大小=N，通道数=C，或者说特征图数目，图像的长宽H*W。不同于C的数组，tensor的下标是从1开始的，而不是C的0

tensor的类型包括了：
–ByteTensor – contains unsigned chars
–CharTensor – contains signed chars
–ShortTensor – contains shorts
–IntTensor – contains ints
–LongTensor – contains longs
–FloatTensor – contains floats
–DoubleTensor – contains doubles

如何定义一个tensor:
x=torch.Tensor(n1,n2,n3,..,nk)
x:size()返回x这个tensor的大小

x:size(n)返回x第n维的大小
x:dim()返回k,x是几维的张量

如何访问一个tensor里的元素
x[n][k],或者采用x:storage()[x:storageOffset()+(n-1)*x:stride(1)+(k-1)*x:stride(2)（更慢）

tensor其实是sotrage的一种特殊的视角，is a particular way of viewing a strogage:a storage only represents a chunk of memory ,while the Tensor interprets this chunk of memory as having dimensions

tensor 赋初值：

x = torch.Tensor(4,5)s = x:storage()for i=1,s:size() do -- fill up the Storage  s[i] = iend> x -- s is interpreted by x as a 2D matrix  1   2   3   4   5  6   7   8   9  10 11  12  13  14  15 16  17  18  19  20[torch.DoubleTensor of dimension 4x5]

x = torch.Tensor(4,5)i = 0x:apply(function()  i = i + 1  return iend)> x  1   2   3   4   5  6   7   8   9  10 11  12  13  14  15 16  17  18  19  20[torch.DoubleTensor of dimension 4x5]> x:stride() 5 1  -- element in the last dimension are contiguous![torch.LongStorage of size 2]

Most numeric operations are implemented only for FloatTensor and DoubleTensor. Other Tensor types are useful if you want to save memory space

使用如下命令设置缺省的tensor类型：
torch.setdefaulttensortype(‘torch.FloatTensor’)

tensor赋值问题
x=torch.Tensor(5):zero()
x:zero()
x:narrow(1,2,3):fill(1)

y = torch.Tensor(x:size()):copy(x)

y = x:clone()

torch.Tensor() create new tensor object,分配新的内存
torch.Tensor(tensor) 类似指针，新创建的tensor和原来的tensor是共享同一块内存的。

x=torch.Tensor(3,4) y=torch.Tensor(x) y[3][2]=1                                                                     [0.0001s] x[3][2]1

x = torch.Tensor(torch.LongStorage({4,4,3,2}))x:size()4432

torch.Tensor(storage,[storageOffset,sizes,[strides]])

s=torch.Storage(10)

x=torch.Tensor(s,1,torch.LongStorage{2,5}),x是s的另一种排列方式
x,s共享

torch.Tensor(table)> torch.Tensor({{1,2,3,4}, {5,6,7,8}}) 1  2  3  4 5  6  7  8[torch.DoubleTensor of dimension 2x4]

tensor的操作/manipulate

1. clone()
   y=x:clone(), x和y不共享地址

2. contiguous():如果原来的存储是连续的地址，那么不拷贝，否则拷贝
   If the given Tensor contents are contiguous in memory, returns the exact same Tensor (no memory copy).
   Otherwise (not contiguous in memory), returns a clone (memory copy).

3. t()转置

x = torch.Tensor(2,3):fill(1)> x 1  1  1 1  1  1[torch.DoubleTensor of dimension 2x3]-- x is contiguous, so y points to the same thingy = x:contiguous():fill(2)> y 2  2  2 2  2  2[torch.DoubleTensor of dimension 2x3]-- contents of x have been changed> x 2  2  2 2  2  2[torch.DoubleTensor of dimension 2x3]-- x:t() is not contiguous, so z is a clonez = x:t():contiguous():fill(3.14)> z 3.1400  3.1400 3.1400  3.1400 3.1400  3.1400[torch.DoubleTensor of dimension 3x2]-- contents of x have not been changed> x 2  2  2 2  2  2[torch.DoubleTensor of dimension 2x3]

1. type() x:type() torch.type(x) x = torch.Tensor(3):fill(3.14)
2. typeAs(tensor)

x = torch.Tensor(3):fill(3.14)> x 3.1400 3.1400 3.1400[torch.DoubleTensor of dimension 3]y = x:type('torch.DoubleTensor')> y 3.1400 3.1400 3.1400[torch.DoubleTensor of dimension 3]-- zero y contentsy:zero()-- contents of x have been changed> x 0 0 0[torch.DoubleTensor of dimension 3]

1. isTensor(obj)

tensor大小

1. nDimension() dim()
2. size(dim) size()
3. #self x=torch.Tensor(2,3) #x—>2 ,3
4. stride(dim) stride()
5. storage()
6. isContiguous()
7. isSize(storage) tensor跟storage的元素数目是否匹配
8. isSameSizeAs(tensor)
9. nElement() tensor的元素数目
10. storageOffset()：
    Return the first index (starting at 1) used in the tensor’s storage.

Referencing a tensor to an existing tensor or chunk of memory
浅拷贝，共享同一块内存,no memory copy

y=torch.Storage(10)
x=torch.Tensor()
x:set(y,1,10)

x=torch.Tensor(y,1,10)

–set(Tensor)
x=torch.Tensor(2,5):fill(3.14)
y=torch.Tensor():set(x)
y:zero()
x也是全零

–isSetTo(tensor)
当且仅当，当两个tensor的size,stride，内存地址和offset是一样的

x=torch.Tesor(2,5)
y=torch.Tensor()
y:isSetTo(x)
y:set(x)
y:isSetTo(x)
y:t():isSetTo(x)

-[self] set(storage,[storageOffset,sizes,[strides]])

s=torch.Storage(10):fill(1)
sz=torch.LongStorage({2,5})
x=torch.Tensor()
x:set(s,1,sz)

–深拷贝copy和初始化
[self]copy(tensor)
x=torch.Tensor(4):fill(1)
y=torch.Tensor(2,2):copy(x)

[self]fill(value)

resizeAs()
resize(sizes)

–Extracting sub-Tensors 提取tensor的子tensor

[self]narrow(dim,index,size),浅拷贝

x = torch.Tensor(5,6):zero()
y=x:narrow(1,2,3)

[Tensor]sub(dims,dimle…[,dim4s[,dim4e]])浅拷贝

[Tensor]select(dim,index)
x=torch.Tensor(5,6):zero()
y=x:select(1,2):fill(2)

[Tensor] [{ dim1,dim2,… }] or [{ {dim1s,dim1e}, {dim2s,dim2e} }]

x[{{},{4,5}}

index 深拷贝

x=torch.rand(5,5)
y=x:index(1,torch.LongTensor{3,1})

indexCopy(dim,index,tensor)

indexAdd(dim,index,tensor)

indexFill(dim,index,val)

gather(dim,index)