utility.md

Torch utility functions

This functions are used in all Torch package for creating and handling classes. The most interesting function is probably torch.class() which allows the user to create easily new classes. torch.typename() might also be interesting to check what is the class of a given Torch object.

The other functions are more for advanced users.

[metatable] torch.class(name, [parentName])

Creates a new Torch class called name. If parentName is provided, the class will inherit parentName methods. A class is a table which has a particular metatable.

If name is of the form package.className then the class className will be added to the specified package. In that case, package has to be a valid (and already loaded) package. If name does not contain any ".", then the class will be defined in the global environment.

One [or two] (meta)tables are returned. These tables contain all the method provided by the class [and its parent class if it has been provided]. After a call to torch.class() you have to fill-up properly the metatable.

After the class definition is complete, constructing a new class name will be achieved by a call to _name_(). This call will first call the method lua__init() if it exists, passing all arguments of _name_().

 require "torch"

 -- for naming convenience
 do
   --- creates a class "Foo"
   local Foo = torch.class('Foo')

   --- the initializer
   function Foo:__init()
     self.contents = "this is some text"
   end

   --- a method
   function Foo:print()
     print(self.contents)
   end

   --- another one
   function Foo:bip()
     print('bip')
   end

 end

 --- now create an instance of Foo
 foo = Foo()

 --- try it out
 foo:print()

 --- create a class torch.Bar which
 --- inherits from Foo
 do
   local Bar, parent = torch.class('torch.Bar', 'Foo')

   --- the initializer
   function Bar:__init(stuff)
     --- call the parent initializer on ourself
     parent.__init(self)

     --- do some stuff
     self.stuff = stuff
   end

   --- a new method
   function Bar:boing()
     print('boing!')
   end

   --- override parent's method
   function Bar:print()
     print(self.contents)
     print(self.stuff)
   end
 end

 --- create a new instance and use it
 bar = torch.Bar("ha ha!")
 bar:print() -- overrided method
 bar:boing() -- child method
 bar:bip()   -- parent's method

For advanced users, it is worth mentionning that torch.class() actually calls torch.newmetatable(). with a particular constructor. The constructor creates a Lua table and set the right metatable on it, and then calls lua__init() if it exists in the metatable. It also sets a factory field lua__factory such that it is possible to create an empty object of this class.

[string] torch.type(object)

Checks if object has a metatable. If it does, and if it corresponds to a Torch class, then returns a string containing the name of the class. Otherwise, it returns the Lua type(object) of the object. Unlike torch.typename(), all outputs are strings:

> torch.type(torch.Tensor())
torch.DoubleTensor
> torch.type({})
table
> torch.type(7)
number

[string] torch.typename(object)

Checks if object has a metatable. If it does, and if it corresponds to a Torch class, then returns a string containing the name of the class. Returns nil in any other cases.

A Torch class is a class created with torch.class() or torch.newmetatable().

[userdata] torch.typename2id(string)

Given a Torch class name specified by string, returns a unique corresponding id (defined by a lightuserdata pointing on the internal structure of the class). This might be useful to do a fast check of the class of an object (if used with torch.id()), avoiding string comparisons.

Returns nil if string does not specify a Torch object.

[userdata] torch.id(object)

Returns a unique id corresponding to the class of the given Torch object. The id is defined by a lightuserdata pointing on the internal structure of the class.

Returns nil if object is not a Torch object.

This is different from the object id returned by torch.pointer().

[boolean] isTypeOf(object, typeSpec)

Checks if a given object is an instance of the type specified by typeSpec. Typespec can be a string (including a string.find pattern) or the constructor object for a Torch class. This function traverses up the class hierarchy, so if b is an instance of B which is a subclass of A, then torch.isTypeOf(b, B) and torch.isTypeOf(b, A) will both return true.

[table] torch.newmetatable(name, parentName, constructor)

Register a new metatable as a Torch type with the given string name. The new metatable is returned.

If the string parentName is not nil and is a valid Torch type (previously created by torch.newmetatable()) then set the corresponding metatable as a metatable to the returned new metatable.

If the given constructor function is not nil, then assign to the variable name the given constructor. The given name might be of the form package.className, in which case the className will be local to the specified package. In that case, package must be a valid and already loaded package.

[function] torch.factory(name)

Returns the factory function of the Torch class name. If the class name is invalid or if the class has no factory, then returns nil.

A Torch class is a class created with torch.class() or torch.newmetatable().

A factory function is able to return a new (empty) object of its corresponding class. This is helpful for object serialization.

[table] torch.getmetatable(string)

Given a string, returns a metatable corresponding to the Torch class described by string. Returns nil if the class does not exist.

A Torch class is a class created with torch.class() or torch.newmetatable().

Example:

> for k,v in pairs(torch.getmetatable("torch.CharStorage")) do print(k,v) end
__index__       function: 0x1a4ba80
__typename      torch.CharStorage
write   function: 0x1a49cc0
__tostring__    function: 0x1a586e0
__newindex__    function: 0x1a4ba40
string  function: 0x1a4d860
__version       1
copy    function: 0x1a49c80
read    function: 0x1a4d840
__len__ function: 0x1a37440
fill    function: 0x1a375c0
resize  function: 0x1a37580
__index table: 0x1a4a080
size    function: 0x1a4ba20

[boolean] torch.isequal(object1, object2)

If the two objects given as arguments are Lua tables (or Torch objects), then returns true if and only if the tables (or Torch objects) have the same address in memory. Returns false in any other cases.

A Torch class is a class created with torch.class() or torch.newmetatable().

[string] torch.getdefaulttensortype()

Returns a string representing the default tensor type currently in use by Torch7.

[table] torch.getenv(function or userdata)

Returns the Lua table environment of the given function or the given userdata. To know more about environments, please read the documentation of lua_setfenv() and lua_getfenv().

[number] torch.version(object)

Returns the field lua__version of a given object. This might be helpful to handle variations in a class over time.

[number] torch.pointer(object)

Returns a unique id (pointer) of the given object, which can be a Torch object, a table, a thread or a function.

This is different from the class id returned by torch.id().

torch.setdefaulttensortype([typename])

Sets the default tensor type for all the tensors allocated from this point on. Valid types are:

• torch.ByteTensor
• torch.CharTensor
• torch.ShortTensor
• torch.IntTensor
• torch.FloatTensor
• torch.DoubleTensor

torch.setenv(function or userdata, table)

Assign table as the Lua environment of the given function or the given userdata. To know more about environments, please read the documentation of lua_setfenv() and lua_getfenv().

[object] torch.setmetatable(table, classname)

Set the metatable of the given table to the metatable of the Torch object named classname. This function has to be used with a lot of care.

[table] torch.getconstructortable(string)

BUGGY Return the constructor table of the Torch class specified by 'string'.

[table] torch.totable(object)

Converts a Tensor or a Storage to a lua table. Also available as methods: tensor:totable() and storage:totable(). Multidimensional Tensors are converted to a set of nested tables, matching the shape of the source Tensor.

t7> print(torch.totable(torch.Tensor({1, 2, 3})))
{
  1 : 1
  2 : 2
  3 : 3
}