Awesome

torchx

This package contains various torch extensions:

concat : concatenates a table of tensors.
find : finds all indices of a given value.
group : sorts and groups similar tensor variables together.
remap : recursively applies a function to tables of Tensors.
md5 : used for hashing strings.

And some paths extensions :

indexdir : index a directory of millions of files for faster listing.

<a name='torch.concat'/> ### [res] torch.concat([res], tensors, [dim]) ### Concatenates a table of Tensors along dimension `dim`. * `res` is a tensor holding the concatenation of Tensors `tensor`. * `tensors` is a table of tensors. Each tensor should have the same amount of dimensions and the same size for non-`dim` dimensions. * `dim` is the dimension along which the tensors will be concatenated. Defaults to 1.

Example:

> res = torch.concat({torch.rand(2,3),torch.randn(2,1),torch.randn(2,2)},2)
> print(res)
 0.8621  0.7776  0.3284 -1.2884 -0.4939  0.6049
 0.8404  0.8996  0.5704  0.3911 -0.0428 -1.4627
[torch.DoubleTensor of dimension 2x6]

<a name='torch.find'/> ### [res] torch.find(tensor, val, [dim]) ### Finds all indices of a given value `val` in Tensor `tensor`. Returns a table of these indices by traversing the tensor one row at a time. When `dim=2`, the only valid value for dim other than `nil` (the default), the function expects a matrix and returns the row-wise indices of each found value `val` in the row.

1D example:

> res = torch.find(torch.Tensor{1,2,3,1,1,2}, 1)
> unpack(res)
1  4  5

2D example:

> tensor = torch.Tensor{{1,2,3,4,5},{5,6,0.6,0,2}}
> unpack(torch.find(tensor, 2))
2	10	
> unpack(torch.find(tensor:t(), 2))
3	10	
> unpack(torch.find(tensor, 2, 2))
{2}  {5}
> unpack(torch.find(tensor:t(), 2, 2))
{ }  {1}  { }  { }  {2}

<a name='torch.group'/> ### [res, val, idx] torch.group([val, idx], tensor, [samegrp, desc]) ### Sorts and groups similar tensor variables together. * `res` is a table of `{idx=torch.LongTensor,val=torch.Tensor}`. * `val` is a Tensor of the same type as `tensor`. It will be used to store and return the sorted values. * `idx` is a `torch.LongTensor` used to store the sorted indices. * `tensor` is a Tensor that will have its values sorted, and then grouped by the `samegrp` function. * `samegrp` is a function taking two argument : `first_val` is the first value of the current group, while `val` is the current value of the current group. When the function returns true, it is assumed that `val` is of the same group as `first_val`. Defaults to `function(first_val, val) return first_val == val; end` * `desc` is a boolean indicating whether the `tensor` gets sorted in descending order. Defaults to false.

Example:

> tensor = torch.Tensor{5,3,4,5,3,5}
> res, val, idx = torch.group(tensor)
> res
{
  3 : 
    {
      idx : LongTensor - size: 2
      val : DoubleTensor - size: 2
    }
  4 : 
    {
      idx : LongTensor - size: 1
      val : DoubleTensor - size: 1
    }
  5 : 
    {
      idx : LongTensor - size: 3
      val : DoubleTensor - size: 3
    }
}

<a name='torch.remap'/> ### [t1, t2] torch.remap(t1, t2, f(x,y) [p1, p2]) ### Recursively applies function `f(x,y)` [to tables [of tables,...] of] Tensors `t1` and `t2`. When prototypes `p1` or `p2` are provided, they are used to initialized any missing Tensors in `t1` or `t2`.

Example:

> t1 = {torch.randn(3,4), {torch.randn(3,4), torch.randn(2,4), {torch.randn(1)}}}
> t2 = {torch.randn(3,4), {torch.randn(3,4), torch.randn(2,4), {torch.randn(1)}}}
> torch.remap(t1, t2, function(x, y) x:add(y) end)
{
  1 : DoubleTensor - size: 3x4
  2 : 
    {
      1 : DoubleTensor - size: 3x4
      2 : DoubleTensor - size: 2x4
      3 : 
        {
          1 : DoubleTensor - size: 1
        }
    }
}
{
  1 : DoubleTensor - size: 3x4
  2 : 
    {
      1 : DoubleTensor - size: 3x4
      2 : DoubleTensor - size: 2x4
      3 : 
        {
          1 : DoubleTensor - size: 1
        }
    }
}

It also creates missing tensors:

> t2, t3 = torch.remap(t2, nil, function(x, y) y:resizeAs(x):copy(x) end)
> print(t3)
{
  1 : DoubleTensor - size: 3x4
  2 : 
    {
      1 : DoubleTensor - size: 3x4
      2 : DoubleTensor - size: 2x4
      3 : 
        {
          1 : DoubleTensor - size: 1
        }
    }
}

When in doubt, first tensor has priority:

> t4, t2 = torch.remap({torch.DoubleTensor()}, t2, function(x, y) x:resize(y:size()):copy(y) end, torch.LongTensor())
> print(t4)
{
  1 : DoubleTensor - size: 3x4
}
> t2, t5 = torch.remap(t2, {torch.DoubleTensor()}, function(x, y) y:resize(x:size()):copy(x) end, torch.LongTensor())
> print(t5)
{
  1 : DoubleTensor - size: 3x4
  2 : 
    {
      1 : LongTensor - size: 3x4
      2 : LongTensor - size: 2x4
      3 : 
        {
          1 : LongTensor - size: 1
        }
    }
}

<a name='torch.md5'/> ### torch.md5 ##

Pure Lua module copy-pasted from this repo (for some reasons I can't get git submodule to work with luarocks). The module includes two functions:

local md5_as_hex   = torch.md5.sumhexa(message)   -- returns a hex string
local md5_as_data  = torch.md5.sum(message)     -- returns raw bytes

The torch.md5.sumhexa function takes a string and returns another string:

torch.md5.sumhexa('helloworld!')
420e57b017066b44e05ea1577f6e2e12

<a name="paths.indexdir"/> ### [obj] paths.indexdir(path, [ext, use_cache, ignore]) ### ```lua files = paths.indexdir("/path/to/files/", 'png', true) images = {} for i=1,files:size() do local img = image.load(files:filename(i)) table.insert(images, img) end ```

This function can be used to create an object indexing all files having extensions ext (a string or a list thereof) in directory path (string or list thereof). Useful for directories containing many thousands of files. The function caches the resulting list to disk in /tmp such that it can be used for later calls when use_cache=true (default is false). Argument ignore species a pattern to ignore (e.g. "frame" will ignore all files containing "frame").