Merge pull request #133 from nicholas-leonard/weightedeuclidean2

WeightedEuclidean optimizations

1 files changed, 68 insertions, 25 deletions

diff --git a/test.lua b/test.lua
index 65ff3b1..f2b3e90 100644
--- a/test.lua
+++ b/test.lua
@@ -511,44 +511,87 @@ function nntest.Euclidean()
 end
 
 function nntest.WeightedEuclidean()
-   local ini = math.random(3,5)
-   local inj = math.random(13,5)
-   local input = torch.Tensor(ini):zero()
+   local ini = math.random(5,7)
+   local inj = math.random(5,7)
+   local input = torch.randn(ini)
+   local gradOutput = torch.randn(inj)
    local module = nn.WeightedEuclidean(ini,inj)
 
+   local output = module:forward(input):clone()
+   
+   local output2 = torch.Tensor(inj):zero()
+   local temp = input:clone()
+   for o = 1,module.weight:size(2) do
+      temp:copy(input):add(-1,module.weight:select(2,o))
+      temp:cmul(temp)
+      temp:cmul(module.diagCov:select(2,o)):cmul(module.diagCov:select(2,o))
+      output2[o] = math.sqrt(temp:sum())
+   end
+   mytester:assertTensorEq(output, output2, 0.000001, 'WeightedEuclidean forward 1D err')
+   
+   local input2 = torch.randn(8, ini)
+   input2[2]:copy(input)
+   local output2 = module:forward(input2)
+   mytester:assertTensorEq(output2[2], output, 0.000001, 'WeightedEuclidean forward 2D err')
+   
+   local output = module:forward(input):clone()
+   module:zeroGradParameters()
+   local gradInput = module:backward(input, gradOutput, 1):clone()
+   local gradInput2 = torch.zeros(ini)
+   for o = 1,module.weight:size(2) do
+      temp:copy(input)
+      temp:add(-1,module.weight:select(2,o))
+      temp:cmul(module.diagCov:select(2,o)):cmul(module.diagCov:select(2,o))
+      temp:mul(gradOutput[o]/output[o])
+      gradInput2:add(temp)
+   end
+   mytester:assertTensorEq(gradInput, gradInput2, 0.000001, 'WeightedEuclidean updateGradInput 1D err')
+   
+   local gradWeight = module.gradWeight:clone():zero()
+   local gradDiagCov = module.gradDiagCov:clone():zero()
+   for o = 1,module.weight:size(2) do
+      if output[o] ~= 0 then
+         temp:copy(module.weight:select(2,o)):add(-1,input)
+         temp:cmul(module.diagCov:select(2,o)):cmul(module.diagCov:select(2,o))
+         temp:mul(gradOutput[o]/output[o])
+         gradWeight:select(2,o):add(temp)
+
+         temp:copy(module.weight:select(2,o)):add(-1,input)
+         temp:cmul(temp)
+         temp:cmul(module.diagCov:select(2,o))
+         temp:mul(gradOutput[o]/output[o])
+         gradDiagCov:select(2,o):add(temp)
+      end
+   end
+   mytester:assertTensorEq(gradWeight, module.gradWeight, 0.000001, 'WeightedEuclidean accGradParameters gradWeight 1D err')
+   mytester:assertTensorEq(gradDiagCov, module.gradDiagCov, 0.000001, 'WeightedEuclidean accGradParameters gradDiagCov 1D err')
+   
+   local input2 = input:view(1, -1):repeatTensor(8, 1)
+   local gradOutput2 = gradOutput:view(1, -1):repeatTensor(8, 1)
+   local output2 = module:forward(input2)
+   module:zeroGradParameters()
+   local gradInput2 = module:backward(input2, gradOutput2, 1/8)
+   mytester:assertTensorEq(gradInput2[2], gradInput, 0.000001, 'WeightedEuclidean updateGradInput 2D err')
+   
+   mytester:assertTensorEq(gradWeight, module.gradWeight, 0.000001, 'WeightedEuclidean accGradParameters gradWeight 2D err')
+   mytester:assertTensorEq(gradDiagCov, module.gradDiagCov, 0.000001, 'WeightedEuclidean accGradParameters gradDiagCov 2D err')
+   
+   input:zero()
+   module.fastBackward = false
+   
    local err = jac.testJacobian(module,input)
    mytester:assertlt(err,precision, 'error on state ')
 
    local err = jac.testJacobianParameters(module, input, module.weight, module.gradWeight)
    mytester:assertlt(err,precision, 'error on weight ')
 
-   local err = jac.testJacobianParameters(module, input, module.bias, module.gradBias)
+   local err = jac.testJacobianParameters(module, input, module.diagCov, module.gradDiagCov)
    mytester:assertlt(err,precision, 'error on bias ')
 
    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 ')
    
-   -- test batch
-   local bs = math.random(3,5)
-   input:uniform(0,1)
-   local output = module:forward(input):clone()
-   module:zeroGradParameters()
-   local gradInput = module:backward(input, output):clone()
-   local params, gradParams = module:parameters()
-   for i=1,#params do
-      params[i] = params[i]:clone()
-   end
-   local input2 = input:view(1, -1):repeatTensor(bs, 1)
-   local output2 = module:forward(input2)
-   module:zeroGradParameters()
-   local gradInput2 = module:backward(input2, output2, 1/bs)
-   local params2, gradParams2 = module:parameters()
-   mytester:assertTensorEq(output2[bs-1], output, 0.000001, "error in batch updateOutput")
-   mytester:assertTensorEq(gradInput2[bs-1], gradInput, 0.000001, "error in batch updateGradInput")
-   mytester:assertTensorEq(gradParams[1], gradParams2[1], 0.000001, "error in batch accGradParameters (gradTemplates)")
-   mytester:assertTensorEq(gradParams[2], gradParams2[2], 0.000001, "error in batch accGradParameters (gradDiagCov)")
-   
    input:zero()
    module:zeroGradParameters()
    local err = jac.testJacobian(module,input)
@@ -557,7 +600,7 @@ function nntest.WeightedEuclidean()
    local err = jac.testJacobianParameters(module, input, module.weight, module.gradWeight)
    mytester:assertlt(err,precision, 'error on weight ')
 
-   local err = jac.testJacobianParameters(module, input, module.bias, module.gradBias)
+   local err = jac.testJacobianParameters(module, input, module.diagCov, module.gradDiagCov)
    mytester:assertlt(err,precision, 'error on bias ')
 
    local ferr,berr = jac.testIO(module,input2)