From d3fe672c549a33527d40f1eb6036f4beb7407f9f Mon Sep 17 00:00:00 2001
From: Soumith Chintala <soumith@fb.com>
Date: Mon, 18 May 2015 16:35:14 -0400
Subject: [PATCH] Revert "Adding Batch L2 Normalization Layer that makes all
 rows of input Tensor unit L2 norm"

This reverts commit 905ea8c1a4033af2af0f90e92e16597f442f3512.
---
 L2Normalize.lua | 40 ----------------------------------------
 init.lua        |  1 -
 test.lua        | 23 -----------------------
 3 files changed, 64 deletions(-)
 delete mode 100644 L2Normalize.lua

diff --git a/L2Normalize.lua b/L2Normalize.lua
deleted file mode 100644
index f1dfd0e99..000000000
--- a/L2Normalize.lua
+++ /dev/null
@@ -1,40 +0,0 @@
-
---[[ 
-   This layer expects an [n x d] Tensor and normalizes each
-   row to have unit L2 norm.
-]]--
-local L2Normalize, parent = torch.class('nn.L2Normalize', 'nn.Module')
-function L2Normalize:__init()
-   parent.__init(self)
-end
-function L2Normalize:updateOutput(input)
-   assert(input:dim() == 2, 'only mini-batch supported (2D tensor), got '
-             .. input:dim() .. 'D tensor instead')
-   self.output:resizeAs(input)
-   self.buffer = self.buffer or input.new()
-   self.normSquared = self.normSquared or input.new()
-   self.normSquared:sum(self.buffer:cmul(input, input), 2)
-   self.buffer:sqrt(self.normSquared)
-   self.output:copy(input):cdiv(self.buffer:expandAs(input))
-   return self.output
-end
-
-function L2Normalize:updateGradInput(input, gradOutput)
-   assert(input:dim() == 2, 'only mini-batch supported')
-   assert(gradOutput:dim() == 2, 'only mini-batch supported')
-   local n = input:size(1) -- batch size
-   local d = input:size(2) -- dimensionality of vectors
-   -- compute diagonal term
-   self.eye = self.eye or torch.eye(d):typeAs(input):repeatTensor(n,1):view(n,d,d)
-   self.diag = self.diag or self.eye.new()
-   self.diag:cmul(self.eye, self.normSquared:view(n,1,1):expand(n,d,d))
-   -- compute cross term
-   local b1 = input:view(n,d,1)
-   local b2 = input:view(n,1,d)
-   self.diag:add(-torch.bmm(b1,b2))
-   -- compute the local gradient of the L2 transformation
-   self.diag:cdiv(torch.pow(self.buffer,3):view(n,1,1):expand(n,d,d))
-   -- chain the gradient
-   self.gradInput:resize(n,d,1):bmm(self.diag, gradOutput:view(n,d,1)):resize(n,d)
-   return self.gradInput
-end
diff --git a/init.lua b/init.lua
index 520b66e2a..b1d36dbab 100644
--- a/init.lua
+++ b/init.lua
@@ -46,7 +46,6 @@ include('WeightedEuclidean.lua')
 include('PairwiseDistance.lua')
 include('CosineDistance.lua')
 include('DotProduct.lua')
-include('L2Normalize.lua')
 
 include('Exp.lua')
 include('Log.lua')
diff --git a/test.lua b/test.lua
index e69d3c8ea..94033ddfb 100644
--- a/test.lua
+++ b/test.lua
@@ -3708,29 +3708,6 @@ function nntest.Padding()
    mytester:assertTensorEq(gradInput, input, 0.00001, "Padding backward error")
 end
 
-function nntest.L2Normalize()
-   local ini = math.random(6,8)
-   local inj = math.random(3,5)
-   local input = torch.randn(ini, inj)
-
-   local module = nn.L2Normalize()
-
-   -- test correctness of output
-   local output = module:forward(input)
-   local norms = torch.norm(output, 2, 2)
-   local desired_norms = torch.ones(ini)
-   mytester:assertTensorEq(norms, desired_norms, 0.000001, 'L2Normalize forward err')
-
-   -- test the Jacobian
-   local err = jac.testJacobian(module,input)
-   mytester:assertlt(err, precision, 'error on state ')
-
-   -- test IO correctness
-   local ferr, berr = jac.testIO(module,input)
-   mytester:asserteq(ferr, 0, torch.typename(module) .. ' - i/o forward err ')
-   mytester:asserteq(berr, 0, torch.typename(module) .. ' - i/o backward err ')
-end
-
 mytester:add(nntest)
 
 if not nn then