Data structures which do not rely on Lua memory allocator, nor being limited by Lua garbage collector.
Only C types can be stored: supported types are currently number, strings, the data structures themselves (see nesting: e.g. it is possible to have a Hash containing a Hash or a Vec), and torch tensors and storages. All data structures can store heterogeneous objects, and support torch serialization.
It is easy to extend the support to other C types.
Note that tds
relies currently on FFI, and works both with
luajit or Lua 5.2, provided
the latter is installed with
luaffi. The dependency on FFI will
be removed in the future.
Creates a hash table which implements the lua operators [key]
, #
and pairs()
, and in very similar way than lua tables.
A hash can contain any element (either as key or value) supported by tds
.
If a lua table tbl
is provided, the Hash will be filled up with
corresponding elements. Tables inside the tbl
will be also converted
(recursively) to tds.Vec (if they contain only contiguous
number keys starting from 1) or tds.Hash otherwise.
Store the given (key
, value
) pair in the hash table. If value
is
nil
, remove the key
if it exists.
Returns the value
at the given key
, and nil
if the key
does not exist in the hash table.
Returns the number of key-value pairs in the hash table. Note that this acts different than lua tables, the latter returning the number of elements stored in numbered indices starting from 1.
Returns an iterator over the hash table d
. The iterator returns a
key-value pair at each step, or nil if reaching the end. Typical usage
will be:
for k,v in pairs(d) do
-- <do something>
end
Note: as for Lua standard tables, the iterator behavior is undefined if a new key is inserted in the hash while iterating. Modifying existing keys is however allowed.
## d = tds.Vec([... || tbl]) ##Creates a vector of elements indexed by numbers starting from 1. If a
single lua table tbl
(or several arguments) is (are) passed at
construction, the vector will be filled with the lua table contents (or the
given arguments).
If provided, tbl
must contain only contiguous number keys starting at 1.
Tables inside the tbl
(or passed as arguments) will also be converted
(recursively) to tds.Vec (if they contain only contiguous
number keys starting from 1) or tds.Hash otherwise.
A vector can contain any element (as value) supported by tds
, as well as
the nil
value.
Store the given value
at the given index
(which must be a positive
number). If the index is larger than the current size of the vector, the
vector will be automatically resized. value
may be nil
.
Returns the value
at the given index
or nil
if it does not exist.
Returns the current size of the vector (note that it includes nil
values, which are not treated as holes!).
Resize the current vector to the given size. If the size is larger than the current size, the vector will be filled with nil
values.
Insert value
in the vector, at position index
, shifting up all elements
above index
. If index
is not provided, insert the element at the end of
the vector.
Remove the element at position index
, shifting down all elements above
index
. If index
is not provided, remove the last element of the vector.
Sort the vector in-place, according to the given compare
function.
Compare can be either a lua function or a C function.
In the lua case, compare
is a function which takes two vector elements,
and returns true when the first is less than the second.
If the C case, compare
must be a FFI type int (*compare)(const tds_elem *, const tds_elem *)
.
It must return an integer less than, equal to, or greater than zero if the
first argument is considered to be respectively less than, equal to, or
greater than the second. See the include file tds_elem.h
for more details
about the tds_elem
structure.
Note that having compare
as a lua function will lead to a (relatively)
slow sort: elements of the vector will need to be moved in the lua userland
(and thus handled by the GC) in order to be compared.
In the FFI case, compare
might be a FFI callback, but will also lead to a
slow sort, FFI callbacks being slow. Fastest speed are obtained when
compare
is a true compiled C function loaded through FFI.
Concat all vector elements into a single string. Fails if an element cannot
be converted via tostring()
.
sep
is an optional separator string inserted between each elements.
i
and j
define an optional range (by default i=1
and j
is the size
of the vector).
As concat(), but returns a torch.CharStorage()
instead.
Returns an iterator over the vector d
. The iterator returns a index-value
pair at each step, or nil if reaching the end. Typical usage will be:
for i,v in pairs(d) do
-- <do something>
end
Alias for ipairs(d).
## Serialization ##All tds
data structures support torch serialization. Example:
tds = require 'tds'
require 'torch'
-- create a vector containing heterogeneous data
d = tds.Vec(4, 5, torch.rand(3), nil, "hello world")
-- serialize in a buffer
f = torch.MemoryFile("rw")
f:writeObject(d)
-- unserialize
f:seek(1)
print(f:readObject())
The example will output:
tds.Vec[5]{
1 : 4
2 : 5
3 : 0.1665
0.8750
0.7525
[torch.DoubleTensor of size 3]
4 : nil
5 : hello world
}
Nesting is supported in tds
. However, reference loops are prohibited, and
will lead to leaks if used.
Example:
tds = require 'tds'
require 'torch'
-- create a vector containing heterogeneous data
d = tds.Vec(4, 5, torch.rand(3), tds.Hash(), "hello world")
-- fill up the hash table:
d[4].foo = "bar"
d[4][6] = torch.rand(3)
d[4].stuff = tds.Vec("how", "are", "you", "doing")
print(d)
This example will output:
tds.Vec[5]{
1 : 4
2 : 5
3 : 0.1958
0.5663
0.2777
[torch.DoubleTensor of size 3]
4 : tds.Hash[3]{
foo : bar
6 : 0.0105
0.7496
0.5241
[torch.DoubleTensor of size 3]
stuff : tds.Vec[4]{
1 : how
2 : are
3 : you
4 : doing
}
}
5 : hello world
}
tds
provides a way to extend to your own C types using the submodule
tds.elem
:
local elem = require 'tds.elem'
tds
typechecking is achieved using this function. You can override it for
your own purposes. If torch is detected, tds
will set elem.type
to
torch.typename()
, so in general (if you are using torch!) you should not
worry about this part.
Add a new C type into tds
:
ttype
must be the typename understood by the currentelem.type()
function.free_p
is a C FFI pointer to a destructor of the C object.setfunc(luaobj)
takes a lua object and returns a FFI C pointer on this object, as well as a FFI functionfree_p
to free this object.getfunc(cpointer)
takes a C FFI pointer and returns a lua object of the corresponding object.
One must be careful to handle properly reference counting and garbage collection in setfunc()
and getfunc()
:
setfunc()
will convert a lua object into a C pointer which will be stored into the data structure: the reference count on this object must be increased. When removed from the data structure,tds
will call the givenfree_p()
function.getfunc()
will convert a C pointer and push it into lua memory space: one must again increase properly the reference count on this object, and make sure lua will garbage collect it properly.
Here is a typical example showing how support for tds.Hash
elements is supported:
elem.addctype(
'tds.Hash',
C.tds_hash_free,
function(lelem)
C.tds_hash_retain(lelem)
return lelem, C.tds_hash_free
end,
function(lelem_p)
local lelem = ffi.cast('tds_hash&', lelem_p)
C.tds_hash_retain(lelem)
ffi.gc(lelem, C.tds_hash_free)
return lelem
end
)