Mode recusiveClone to utils

Author: fmassa

init.lua

@@ -6,20 +6,6 @@ require 'optnet.tests'
 
 local utils = require 'optnet.utils'
 
-local function recursiveClone(out)
-  if torch.isTensor(out) then
-    return out:clone()
-  else
-    local res = {}
-    for k, v in ipairs(out) do
-      res[k] = recursiveClone(v)
-    end
-    return res
-  end
-end
-
-
-
 local kNotUsed = 10000---1
 local kNotDefined = 0
 local kMinimumForSharing = 2
@@ -147,7 +133,7 @@ local function analyse(net, input, opts)
   local out = net['forward'](net, input)
   local grad
   if mode == 'training' then
-    grad = recursiveClone(out)
+    grad = utils.recursiveClone(out)
     net['backward'](net, input, grad)
   end
   local function trackInputs(t, name)
@@ -362,7 +348,7 @@ function optnet.optimizeMemory(net, input, opts)
   local out = net['forward'](net, input)
   local grad
   if mode == 'training' then
-    grad = recursiveClone(out)
+    grad = utils.recursiveClone(out)
     net['backward'](net, input, grad)
   end
 

tests.lua

@@ -1,5 +1,6 @@
 local optnet = require 'optnet.env'
 local models = require 'optnet.models'
+local utils = require 'optnet.utils'
 local countUsedMemory = optnet.countUsedMemory
 
 local optest = torch.TestSuite()
@@ -23,20 +24,6 @@ local function resizeAndConvert(input, type)
   return res
 end
 
--- reuse this function
-local function recursiveClone(out)
-  if torch.isTensor(out) then
-    return out:clone()
-  else
-    local res = {}
-    for k, v in ipairs(out) do
-      res[k] = recursiveClone(v)
-    end
-    return res
-  end
-end
-
-
 local function cudnnSetDeterministic(net)
   net:apply(function(m)
     if m.setMode then m:setMode(1, 1, 1) end
@@ -54,13 +41,13 @@ local function genericTestForward(model,opts)
     input = resizeAndConvert(input,'torch.CudaTensor')
   end
 
-  local out_orig = recursiveClone(net:forward(input))
+  local out_orig = utils.recursiveClone(net:forward(input))
 
   local mems1 = optnet.countUsedMemory(net)
 
   optnet.optimizeMemory(net, input)
 
-  local out = recursiveClone(net:forward(input))
+  local out = utils.recursiveClone(net:forward(input))
   local mems2 = countUsedMemory(net)
   tester:eq(out_orig, out, 'Outputs differ after optimization of '..model)
 
@@ -101,21 +88,21 @@ local function genericTestBackward(model,opts)
     input = resizeAndConvert(input,'torch.CudaTensor')
   end
 
-  local out_orig = recursiveClone(net:forward(input))
-  local grad_orig = recursiveClone(out_orig)
+  local out_orig = utils.recursiveClone(net:forward(input))
+  local grad_orig = utils.recursiveClone(out_orig)
   net:zeroGradParameters()
-  local gradInput_orig = recursiveClone(net:backward(input, grad_orig))
+  local gradInput_orig = utils.recursiveClone(net:backward(input, grad_orig))
   local _, gradParams_orig = net:getParameters()
   gradParams_orig = gradParams_orig:clone()
 
   local mems1 = optnet.countUsedMemory(net)
 
   optnet.optimizeMemory(net, input, {mode='training'})
 
-  local out = recursiveClone(net:forward(input))
-  local grad = recursiveClone(out)
+  local out = utils.recursiveClone(net:forward(input))
+  local grad = utils.recursiveClone(out)
   net:zeroGradParameters()
-  local gradInput = recursiveClone(net:backward(input, grad))
+  local gradInput = utils.recursiveClone(net:backward(input, grad))
   local _, gradParams = net:getParameters()
   gradParams = gradParams:clone()
 
@@ -165,20 +152,20 @@ local function genericTestRemoveOptim(model,opts)
     input = resizeAndConvert(input,'torch.CudaTensor')
   end
 
-  local out_orig = recursiveClone(net:forward(input))
-  local grad_orig = recursiveClone(out_orig)
+  local out_orig = utils.recursiveClone(net:forward(input))
+  local grad_orig = utils.recursiveClone(out_orig)
   net:zeroGradParameters()
-  local gradInput_orig = recursiveClone(net:backward(input, grad_orig))
+  local gradInput_orig = utils.recursiveClone(net:backward(input, grad_orig))
   local _, gradParams_orig = net:getParameters()
   gradParams_orig = gradParams_orig:clone()
 
   optnet.optimizeMemory(net, input)
   optnet.removeOptimization(net)
 
-  local out = recursiveClone(net:forward(input))
-  local grad = recursiveClone(out)
+  local out = utils.recursiveClone(net:forward(input))
+  local grad = utils.recursiveClone(out)
   net:zeroGradParameters()
-  local gradInput = recursiveClone(net:backward(input, grad))
+  local gradInput = utils.recursiveClone(net:backward(input, grad))
   local _, gradParams = net:getParameters()
   gradParams = gradParams:clone()
 

utils.lua

@@ -19,4 +19,17 @@ local function keepTrack(t, track, entry_fun, fun, ...)
 end
 utils.keepTrack = keepTrack
 
+local function recursiveClone(out)
+  if torch.isTensor(out) then
+    return out:clone()
+  else
+    local res = {}
+    for k, v in ipairs(out) do
+      res[k] = recursiveClone(v)
+    end
+    return res
+  end
+end
+utils.recursiveClone = recursiveClone
+
 return utils