removed nnx dependency

Author: Nicholas Leonard

SoftMaxForest.lua

@@ -14,7 +14,7 @@
 local SpatialGlimpse, parent = torch.class("nn.SpatialGlimpse", "nn.Module")
 
 function SpatialGlimpse:__init(size, depth, scale)
-   require 'nnx'
+   dpnn.require('nnx')
    if torch.type(size)=='table' then
       self.height = size[1]
       self.width = size[2]
@@ -42,6 +42,7 @@ end
 -- a bandwidth limited sensor which focuses on a location.
 -- locations index the x,y coord of the center of the output glimpse
 function SpatialGlimpse:updateOutput(inputTable)
+   dpnn.require('nnx')
    assert(torch.type(inputTable) == 'table')
    assert(#inputTable >= 2)
    local input, location = unpack(inputTable)

SoftMaxTree.lua

@@ -1,6 +1,7 @@
 local SpatialUniformCrop, parent = torch.class("nn.SpatialUniformCrop", "nn.Module")
 
 function SpatialUniformCrop:__init(oheight, owidth, scale)
+   dpnn.require('nnx')
    parent.__init(self)
    self.scale = scale or nil
    if self.scale ~= nil then
@@ -12,8 +13,9 @@ function SpatialUniformCrop:__init(oheight, owidth, scale)
 end
 
 function SpatialUniformCrop:updateOutput(input)
+   dpnn.require('nnx')
    input = self:toBatch(input, 3)
-   
+
    self.output:resize(input:size(1), input:size(2), self.oheight, self.owidth)
    self.coord = self.coord or torch.IntTensor()
    self.coord:resize(input:size(1), 2)
@@ -22,7 +24,7 @@ function SpatialUniformCrop:updateOutput(input)
       self.scales = self.scales or torch.FloatTensor()
       self.scales:resize(input:size(1))
    end
-  
+
    local iH, iW = input:size(3), input:size(4)
    if self.train ~= false then
       if self.scale ~= nil then
@@ -34,7 +36,7 @@ function SpatialUniformCrop:updateOutput(input)
 
             local h = math.ceil(torch.uniform(1e-2, iH-soheight))
             local w = math.ceil(torch.uniform(1e-2, iW-sowidth))
-           
+
             local ch = math.ceil(iH/2 - (iH-soheight)/2 + h)
             local cw = math.ceil(iW/2 - (iH-sowidth)/2 + w)
 
@@ -70,15 +72,15 @@ function SpatialUniformCrop:updateOutput(input)
       local crop = input:narrow(3,h1,self.oheight):narrow(4,w1,self.owidth)
       self.output:copy(crop)
    end
-   
+
    self.output = self:fromBatch(self.output, 1)
    return self.output
 end
 
 function SpatialUniformCrop:updateGradInput(input, gradOutput)
    input = self:toBatch(input, 3)
    gradOutput = self:toBatch(gradOutput, 3)
-   
+
    self.gradInput:resizeAs(input):zero()
    if self.scale ~= nil then
       local iH, iW = input:size(3), input:size(4)
@@ -88,7 +90,7 @@ function SpatialUniformCrop:updateGradInput(input, gradOutput)
          local sowidth = math.ceil(s*self.owidth)
 
          local h, w = self.coord[{i,1}], self.coord[{i,2}]
-        
+
          local ch = math.ceil(iH/2 - (iH-soheight)/2 + h)
          local cw = math.ceil(iW/2 - (iH-sowidth)/2 + w)
 
@@ -108,7 +110,7 @@ function SpatialUniformCrop:updateGradInput(input, gradOutput)
          self.gradInput[i]:narrow(2,h1,self.oheight):narrow(3,w1,self.owidth):copy(gradOutput[i])
       end
    end
-   
+
    self.gradInput = self:fromBatch(self.gradInput, 1)
    return self.gradInput
 end

SpatialGlimpse.lua

@@ -1,6 +1,5 @@
 require 'torch'
 require 'nn'
-require 'nnx'
 local _ = require 'moses'
 
 -- create global dpnn table
@@ -9,6 +8,11 @@ dpnn.version = 2
 
 unpack = unpack or table.unpack -- lua 5.2 compat
 
+function dpnn.require(packagename)
+   assert(torch.type(packagename) == 'string')
+   assert(pcall(function() require(packagename) end), "missing package "..packagename..": run 'luarocks install nnx'")
+end
+
 -- for testing:
 require('dpnn.test')
 
@@ -49,8 +53,6 @@ require('dpnn.CAddTensorTable')
 require('dpnn.ReverseTable')
 require('dpnn.Dictionary')
 require('dpnn.Inception')
-require('dpnn.SoftMaxTree')
-require('dpnn.SoftMaxForest')
 require('dpnn.Clip')
 require('dpnn.SpatialUniformCrop')
 require('dpnn.SpatialGlimpse')

SpatialUniformCrop.lua

@@ -898,6 +898,7 @@ end
 
 function dpnntest.SpatialGlimpse()
    if not pcall(function() require "image" end) then return end -- needs the image package
+   if not pcall(function() require "nnx" end) then return end -- needs the nnx package
    local batchSize = 1
    local inputSize = {2,8,8}
    local glimpseSize = 4
@@ -1118,12 +1119,12 @@ function dpnntest.SpatialGlimpse()
 end
 
 function dpnntest.SpatialGlimpse_backwardcompat()
+   if not pcall(function() require "nnx" end) then return end -- needs the nnx package
    -- this is ugly, but I know this verson of the module works.
    -- So we try to match the newer versions to it
    local SG, parent = torch.class("nn.SG", "nn.Module")
 
    function SG:__init(size, depth, scale)
-      require 'nnx'
       self.size = size -- height == width
       self.depth = depth or 3
       self.scale = scale or 2