nn.ZipTable

ZipTable.lua

@@ -0,0 +1,34 @@
+local ZipTable, parent = torch.class('nn.ZipTable', 'nn.Module')
+
+-- input : { {a1,a2}, {b1,b2}, {c1,c2} }
+-- output : { {a1,b1,c1}, {a2,b2,c2} }
+function ZipTable:__init()
+   parent.__init(self)
+   self.output = {}
+   self.gradInput = {}
+end
+
+function ZipTable:updateOutput(inputTable)
+   self.output = {}
+   for i,inTable in ipairs(inputTable) do
+      for j,input in ipairs(inTable) do
+         local output = self.output[j] or {}
+         output[i] = input
+         self.output[j] = output
+      end
+   end
+   return self.output
+end
+
+function ZipTable:updateGradInput(inputTable, gradOutputTable)
+   self.gradInput = {}
+   for i,gradOutTable in ipairs(gradOutputTable) do
+      for j,gradOutput in ipairs(gradOutTable) do
+         local gradInput = self.gradInput[j] or {}
+         gradInput[i] = gradOutput
+         self.gradInput[j] = gradInput
+      end
+   end
+   return self.gradInput
+end
+

ZipTableOneToMany.lua

@@ -0,0 +1,37 @@
+local ZipTableOneToMany, parent = torch.class('nn.ZipTableOneToMany', 'nn.Module')
+
+-- based on ZipTable in dpnn
+
+-- input : { v, {a, b, c} }
+-- output : { {v,a}, {v,b}, {v,c} }
+function ZipTableOneToMany:__init()
+   parent.__init(self)
+   self.output = {}
+   self.gradInput = {}
+   -- make buffer to update during forward/backward
+   self.gradInputEl = torch.Tensor()
+end
+
+function ZipTableOneToMany:updateOutput(input)
+   assert(#input == 2, "input must be table of element and table")
+   local inputEl, inputTable = input[1], input[2]
+   self.output = {}
+   for i,v in ipairs(inputTable) do
+      self.output[i] = {inputEl, v}
+   end
+   return self.output
+end
+
+function ZipTableOneToMany:updateGradInput(input, gradOutput)
+   assert(#input == 2, "input must be table of element and table")
+   local inputEl, inputTable = input[1], input[2]
+   self.gradInputEl:resizeAs(inputEl):zero()
+   local gradInputTable = {}
+   for i,gradV in ipairs(gradOutput) do
+      self.gradInputEl:add(gradV[1])
+      gradInputTable[i] = gradV[2]
+   end
+   self.gradInput = {self.gradInputEl, gradInputTable}
+   return self.gradInput
+end
+

doc/table.md

@@ -15,6 +15,8 @@ This allows one to build very rich architectures:
     * [`SelectTable`](#nn.SelectTable): select one element from a `table`;
     * [`NarrowTable`](#nn.NarrowTable): select a slice of elements from a `table`;
     * [`FlattenTable`](#nn.FlattenTable): flattens a nested `table` hierarchy;
+    * [`ZipTable`](#nn.ZipTable) : zip a table of tables into a table of tables;
+    * [`ZipTableOneToMany`](#nn.ZipTableOneToMany) : zip a table to a single tensor;
   * Pair Modules compute a measure like distance or similarity from a pair (`table`) of input `Tensor`s:
     * [`PairwiseDistance`](#nn.PairwiseDistance): outputs the `p`-norm. distance between inputs;
     * [`DotProduct`](#nn.DotProduct): outputs the dot product (similarity) between inputs;
@@ -692,7 +694,7 @@ Forwarding a batch of 2 examples gives us something like this:
 
 `module` = `SelectTable(index)`
 
-Creates a module that takes a (nested) `table` as input and outputs the element at index `index`. `index` can be strings or integers (positive or negative). 
+Creates a module that takes a (nested) `table` as input and outputs the element at index `index`. `index` can be strings or integers (positive or negative).
 This can be either a `table` or a [`Tensor`](https://github.com/torch/torch7/blob/master/doc/tensor.md#tensor).
 
 The gradients of the non-`index` elements are zeroed `Tensor`s of the same size. This is true regardless of the
@@ -731,7 +733,7 @@ Exmaple 2:
 
 > gradInput = nn.SelectTable("A"):backward(input, torch.randn(2, 3))
 
-> gradInput 
+> gradInput
 {
   A : DoubleTensor - size: 2x3
   B : DoubleTensor - size: 2x1
@@ -811,11 +813,11 @@ Example 3:
 
 `module` = `NarrowTable(offset [, length])`
 
-Creates a module that takes a `table` as input and outputs the subtable 
+Creates a module that takes a `table` as input and outputs the subtable
 starting at index `offset` having `length` elements (defaults to 1 element).
 The elements can be either a `table` or a [`Tensor`](https://github.com/torch/torch7/blob/master/doc/tensor.md#tensor).
 
-The gradients of the elements not included in the subtable are zeroed `Tensor`s of the same size. 
+The gradients of the elements not included in the subtable are zeroed `Tensor`s of the same size.
 This is true regardless of the depth of the encapsulated `Tensor` as the function used internally to do so is recursive.
 
 Example:
@@ -883,6 +885,36 @@ gives the output:
 }
 ```
 
+<a name='nn.ZipTable'></a>
+## ZipTable ##
+
+```lua
+module = nn.ZipTable()
+```
+
+Zips a table of tables into a table of tables.
+
+Example:
+```lua
+print(module:forward{ {'a1','a2'}, {'b1','b2'}, {'c1','c2'} })
+{ {'a1','b1','c1'}, {'a2','b2','c2'} }
+```
+
+<a name='nn.ZipTableOneToMany'></a>
+## ZipTableOneToMany ##
+
+```lua
+module = nn.ZipTableOneToMany()
+```
+
+Zips a table of element `el` and table of elements `tab` into a table of tables, where the i-th table contains the element `el` and the i-th element in table `tab`
+
+Example:
+```lua
+print(module:forward{ 'el', {'a','b','c'} })
+{ {'el','a'}, {'el','b'}, {'el','c'} }
+```
+
 <a name="nn.PairwiseDistance"></a>
 ## PairwiseDistance ##
 

init.lua

@@ -170,6 +170,8 @@ require('nn.CriterionTable')
 require('nn.FlattenTable')
 require('nn.NarrowTable')
 require('nn.MapTable')
+require('nn.ZipTable')
+require('nn.ZipTableOneToMany')
 
 require('nn.Criterion')
 require('nn.MSECriterion')

test.lua

@@ -8548,6 +8548,54 @@ function nntest.ZeroGrad()
    mytester:assertTensorEq(gradInput, gradInput2, 0.0000001)
 end
 
+function nntest.ZipTable()
+   -- input : { {a1,a2}, {b1,b2}, {c1,c2} }
+   -- output : { {a1,b1,c1}, {a2,b2,c2} }
+   local z = nn.ZipTable()
+   local input = {
+      {torch.randn(3,4), torch.randn(3,4)},
+      {torch.randn(3,4), torch.randn(3,4)},
+      {torch.randn(3,4), torch.randn(3,4)}
+   }
+   local output = z:forward(input)
+   mytester:assert(#output == 2, "ZipTable #output")
+   mytester:assert(#(output[1]) == 3, "ZipTable #output[1]")
+   mytester:assertTensorEq(input[1][1], output[1][1], 0.000001, "ZipTable input11")
+   mytester:assertTensorEq(input[1][2], output[2][1], 0.000001, "ZipTable input12")
+   mytester:assertTensorEq(input[3][2], output[2][3], 0.000001, "ZipTable input32")
+   local gradInput = z:backward(input, output)
+   mytester:assert(#gradInput == 3, "ZipTable #gradInput")
+   mytester:assert(#(gradInput[1]) == 2, "ZipTable #gradInput[1]")
+   mytester:assertTensorEq(input[1][1], gradInput[1][1], 0.000001, "ZipTable gradInput11")
+   mytester:assertTensorEq(input[1][2], gradInput[1][2], 0.000001, "ZipTable gradInput12")
+   mytester:assertTensorEq(input[3][2], gradInput[3][2], 0.000001, "ZipTable gradInput32")
+end
+
+function nntest.ZipTableOneToMany()
+   -- input : { v, {a,b,c} }
+   -- output : { {v,a}, {v,b}, {v,c} }
+   local z = nn.ZipTableOneToMany()
+   local input = { torch.randn(3), { torch.randn(4), torch.rand(4), torch.rand(4) } }
+   local output = z:forward(input)
+   mytester:assert(#output == 3, "ZipTableOneToMany #output")
+   mytester:assert(#(output[1]) == 2, "ZipTableOneToMany #output[1]")
+   mytester:assert(#(output[2]) == 2, "ZipTableOneToMany #output[2]")
+   mytester:assert(#(output[3]) == 2, "ZipTableOneToMany #output[3]")
+   mytester:assertTensorEq(input[1], output[1][1], 0.000001, "ZipTableOneToMany input1 output11")
+   mytester:assertTensorEq(input[1], output[2][1], 0.000001, "ZipTableOneToMany input1 output21")
+   mytester:assertTensorEq(input[1], output[3][1], 0.000001, "ZipTableOneToMany input1 output31")
+   mytester:assertTensorEq(input[2][1], output[1][2], 0.000001, "ZipTableOneToMany input21")
+   mytester:assertTensorEq(input[2][2], output[2][2], 0.000001, "ZipTableOneToMany input22")
+   mytester:assertTensorEq(input[2][3], output[3][2], 0.000001, "ZipTableOneToMany input23")
+   local gradInput = z:backward(input, output)
+   mytester:assert(#gradInput == 2, "ZipTableOneToMany #gradInput")
+   mytester:assert(#(gradInput[2]) == 3, "ZipTableOneToMany #gradInput[2]")
+   mytester:assertTensorEq(input[2][1], gradInput[2][1], 0.000001, "ZipTableOneToMany gradInput21")
+   mytester:assertTensorEq(input[2][2], gradInput[2][2], 0.000001, "ZipTableOneToMany gradInput22")
+   mytester:assertTensorEq(input[2][3], gradInput[2][3], 0.000001, "ZipTableOneToMany gradInput32")
+   mytester:assertTensorEq(torch.mul(input[1], 3), gradInput[1], 0.000001, "ZipTableOneToMany gradInput21")
+end
+
 
 mytester:add(nntest)