Queue + recursive functions

Queue.lua

@@ -0,0 +1,28 @@
+local Queue = torch.class("torchx.Queue")
+
+function Queue:__init()
+   self.first = 0
+   self.last = -1
+   self.list = {}
+end
+
+function Queue:put(value)
+   local first = self.first - 1
+   self.first = first
+   self.list[first] = value
+end
+
+function Queue:empty()
+   return self.first > self.last
+end
+
+function Queue:get()
+   local last = self.last
+   if self:empty() then 
+      error("Queue is empty")
+   end
+   local value = self.list[last]
+   self.list[last] = nil  
+   self.last = last - 1
+   return value
+end

init.lua

@@ -16,6 +16,7 @@ torch.include('torchx', 'concat.lua')
 torch.include('torchx', 'indexdir.lua')
 torch.include('torchx', 'dkjson.lua')
 torch.include('torchx', 'recursivetensor.lua')
+torch.include('torchx', 'Queue.lua')
 
 torch.include('torchx', 'test.lua')
 

recursivetensor.lua

@@ -158,16 +158,16 @@ function torchx.recursiveIndex(res, src, dim, indices)
    if torch.type(src) == 'table' then
       res = (torch.type(res) == 'table') and res or {res}
       for key,_ in pairs(src) do
-         res[key], res[key] = torchx.recursiveIndex(res[key], src[key], dim, indices)
+         res[key] = torchx.recursiveIndex(res[key], src[key], dim, indices)
       end
    elseif torch.isTensor(src) then
-      res = torch.isTensor(res) or src.new()
+      res = torch.isTensor(res) and res or src.new()
       res:index(src, dim, indices)
    else
       error("expecting nested tensors or tables. Got "..
             torch.type(res).." and "..torch.type(src).." instead")
    end
-   return res, src
+   return res 
 end
 
 -- get the batch size (i.e. size of first dim for a nested tensor)
@@ -179,3 +179,35 @@ function torchx.recursiveBatchSize(input)
       return input:size(1)
    end
 end
+
+function torchx.recursiveSize(input, excludedim)
+   local res
+   if torch.type(input) == 'table' then
+      res = {}
+      for k,v in pairs(input) do
+         res[k] = torchx.recursiveSize(v, excludedim)
+      end
+   else
+      assert(torch.isTensor(input))
+      res = input:size():totable()
+      if excludedim then
+         table.remove(res, excludedim)
+      end
+   end
+   return res
+end
+
+function torchx.recursiveSub(src, start, stop)
+   local res
+   if torch.type(src) == 'table' then
+      res = {}
+      for key,_ in pairs(src) do
+         res[key] = torchx.recursiveSub(src[key], start, stop)
+      end
+   elseif torch.isTensor(src) then
+      res = src:sub(start, stop)
+   else
+      error("expecting nested tensors or tables. Got "..torch.type(src).." instead")
+   end
+   return res
+end