Merge branch 'recursive'

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

@@ -15,6 +15,8 @@ torch.include('torchx', 'group.lua')
 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

@@ -0,0 +1,213 @@
+
+function torchx.recursiveResizeAs(t1,t2)
+   if torch.type(t2) == 'table' then
+      t1 = (torch.type(t1) == 'table') and t1 or {t1}
+      for key,_ in pairs(t2) do
+         t1[key], t2[key] = torchx.recursiveResizeAs(t1[key], t2[key])
+      end
+   elseif torch.isTensor(t2) then
+      t1 = torch.isTensor(t1) and t1 or t2.new()
+      t1:resizeAs(t2)
+   else
+      error("expecting nested tensors or tables. Got "..
+            torch.type(t1).." and "..torch.type(t2).." instead")
+   end
+   return t1, t2
+end
+
+function torchx.recursiveSet(t1,t2)
+   if torch.type(t2) == 'table' then
+      t1 = (torch.type(t1) == 'table') and t1 or {t1}
+      for key,_ in pairs(t2) do
+         t1[key], t2[key] = torchx.recursiveSet(t1[key], t2[key])
+      end
+   elseif torch.isTensor(t2) then
+      t1 = torch.isTensor(t1) and t1 or t2.new()
+      t1:set(t2)
+   else
+      error("expecting nested tensors or tables. Got "..
+            torch.type(t1).." and "..torch.type(t2).." instead")
+   end
+   return t1, t2
+end
+
+function torchx.recursiveCopy(t1,t2)
+   if torch.type(t2) == 'table' then
+      t1 = (torch.type(t1) == 'table') and t1 or {t1}
+      for key,_ in pairs(t2) do
+         t1[key], t2[key] = torchx.recursiveCopy(t1[key], t2[key])
+      end
+   elseif torch.isTensor(t2) then
+      t1 = torch.isTensor(t1) and t1 or t2.new()
+      t1:resizeAs(t2):copy(t2)
+   else
+      error("expecting nested tensors or tables. Got "..
+            torch.type(t1).." and "..torch.type(t2).." instead")
+   end
+   return t1, t2
+end
+
+function torchx.recursiveAdd(t1, t2)
+   if torch.type(t2) == 'table' then
+      t1 = (torch.type(t1) == 'table') and t1 or {t1}
+      for key,_ in pairs(t2) do
+         t1[key], t2[key] = torchx.recursiveAdd(t1[key], t2[key])
+      end
+   elseif torch.isTensor(t1) and torch.isTensor(t2) then
+      t1:add(t2)
+   else
+      error("expecting nested tensors or tables. Got "..
+            torch.type(t1).." and "..torch.type(t2).." instead")
+   end
+   return t1, t2
+end
+
+function torchx.recursiveTensorEq(t1, t2)
+   if torch.type(t2) == 'table' then
+      local isEqual = true
+      if torch.type(t1) ~= 'table' then
+         return false
+      end
+      for key,_ in pairs(t2) do
+          isEqual = isEqual and torchx.recursiveTensorEq(t1[key], t2[key])
+      end
+      return isEqual
+   elseif torch.isTensor(t2) and torch.isTensor(t2) then
+      local diff = t1-t2
+      local err = diff:abs():max()
+      return err < 0.00001
+   else
+      error("expecting nested tensors or tables. Got "..
+            torch.type(t1).." and "..torch.type(t2).." instead")
+   end
+end
+
+function torchx.recursiveNormal(t2)
+   if torch.type(t2) == 'table' then
+      for key,_ in pairs(t2) do
+         t2[key] = torchx.recursiveNormal(t2[key])
+      end
+   elseif torch.isTensor(t2) then
+      t2:normal()
+   else
+      error("expecting tensor or table thereof. Got "
+           ..torch.type(t2).." instead")
+   end
+   return t2
+end
+
+function torchx.recursiveFill(t2, val)
+   if torch.type(t2) == 'table' then
+      for key,_ in pairs(t2) do
+         t2[key] = torchx.recursiveFill(t2[key], val)
+      end
+   elseif torch.isTensor(t2) then
+      t2:fill(val)
+   else
+      error("expecting tensor or table thereof. Got "
+           ..torch.type(t2).." instead")
+   end
+   return t2
+end
+
+function torchx.recursiveType(param, type_str)
+   if torch.type(param) == 'table' then
+      for i = 1, #param do
+         param[i] = torchx.recursiveType(param[i], type_str)
+      end
+   else
+      if torch.typename(param) and 
+        torch.typename(param):find('torch%..+Tensor') then
+         param = param:type(type_str)
+      end
+   end
+   return param
+end
+
+function torchx.recursiveSum(t2)
+   local sum = 0
+   if torch.type(t2) == 'table' then
+      for key,_ in pairs(t2) do
+         sum = sum + torchx.recursiveSum(t2[key], val)
+      end
+   elseif torch.isTensor(t2) then
+      return t2:sum()
+   else
+      error("expecting tensor or table thereof. Got "
+           ..torch.type(t2).." instead")
+   end
+   return sum
+end
+
+function torchx.recursiveNew(t2)
+   if torch.type(t2) == 'table' then
+      local t1 = {}
+      for key,_ in pairs(t2) do
+         t1[key] = torchx.recursiveNew(t2[key])
+      end
+      return t1
+   elseif torch.isTensor(t2) then
+      return t2.new()
+   else
+      error("expecting tensor or table thereof. Got "
+           ..torch.type(t2).." instead")
+   end
+end
+
+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] = torchx.recursiveIndex(res[key], src[key], dim, indices)
+      end
+   elseif torch.isTensor(src) then
+      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 
+end
+
+-- get the batch size (i.e. size of first dim for a nested tensor)
+function torchx.recursiveBatchSize(input)
+   if torch.type(input) == 'table' then
+      return torchx.recursiveBatchSize(input[1])
+   else
+      assert(torch.isTensor(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