Faster unit tests

1 files changed, 227 insertions, 225 deletions

diff --git a/test/test.lua b/test/test.lua
index 99b4bd5..aebc755 100644
--- a/test/test.lua
+++ b/test/test.lua
@@ -21,10 +21,11 @@ local function equal(t1, t2, msg)
    end
 end
 
+
 function nntest.Add()
-   local ini = math.random(10,20)
-   local inj = math.random(10,20)
-   local ink = math.random(10,20)
+   local ini = math.random(3,5)
+   local inj = math.random(3,5)
+   local ink = math.random(3,5)
    local input = torch.Tensor(ini,inj,ink):zero()
    local module = nn.Add(ini*inj*ink)
 
@@ -47,9 +48,9 @@ function nntest.Add()
 end
 
 function nntest.CMul()
-   local ini = math.random(5,15)
-   local inj = math.random(5,15)
-   local ink = math.random(5,15)
+   local ini = math.random(3,5)
+   local inj = math.random(3,5)
+   local ink = math.random(3,5)
    local input = torch.Tensor(ini,inj,ink):zero()
    local module = nn.CMul(ini*inj*ink)
 
@@ -103,9 +104,9 @@ function nntest.ReLU()
 end
 
 function nntest.Exp()
-   local ini = math.random(10,20)
-   local inj = math.random(10,20)
-   local ink = math.random(10,20)
+   local ini = math.random(3,5)
+   local inj = math.random(3,5)
+   local ink = math.random(3,5)
    local input = torch.Tensor(ini,inj,ink):zero()
    local module = nn.Exp()
 
@@ -118,9 +119,9 @@ function nntest.Exp()
 end
 
 function nntest.Log()
-   local ini = math.random(10,20)
-   local inj = math.random(10,20)
-   local ink = math.random(10,20)
+   local ini = math.random(3,5)
+   local inj = math.random(3,5)
+   local ink = math.random(3,5)
    local input = torch.Tensor(ini,inj,ink):zero()
    local module = nn.Log()
 
@@ -133,9 +134,9 @@ function nntest.Log()
 end
 
 function nntest.HardTanh()
-   local ini = math.random(5,10)
-   local inj = math.random(5,10)
-   local ink = math.random(5,10)
+   local ini = math.random(3,5)
+   local inj = math.random(3,5)
+   local ink = math.random(3,5)
    local input = torch.Tensor(ink, inj, ini):zero()
    
    local module = nn.HardTanh()
@@ -149,9 +150,9 @@ function nntest.HardTanh()
 end
 
 function nntest.Abs()
-   local ini = math.random(5,10)
-   local inj = math.random(5,10)
-   local ink = math.random(5,10)
+   local ini = math.random(3,5)
+   local inj = math.random(3,5)
+   local ink = math.random(3,5)
    local input = torch.Tensor(ink, inj, ini):zero()
    
    local module = nn.Abs()
@@ -165,9 +166,9 @@ function nntest.Abs()
 end
 
 function nntest.Threshold()
-   local ini = math.random(5,10)
-   local inj = math.random(5,10)
-   local ink = math.random(5,10)
+   local ini = math.random(3,5)
+   local inj = math.random(3,5)
+   local ink = math.random(3,5)
    local input = torch.Tensor(ink, inj, ini):zero()
 
    local module = nn.Threshold(torch.uniform(-2,2),torch.uniform(-2,2))
@@ -181,9 +182,9 @@ function nntest.Threshold()
 end
 
 function nntest.HardShrink()
-   local ini = math.random(5,10)
-   local inj = math.random(5,10)
-   local ink = math.random(5,10)
+   local ini = math.random(3,5)
+   local inj = math.random(3,5)
+   local ink = math.random(3,5)
    local input = torch.Tensor(ink, inj, ini):zero()
 
    local module = nn.HardShrink(math.random()/2)
@@ -197,9 +198,9 @@ function nntest.HardShrink()
 end
 
 function nntest.SoftShrink()
-   local ini = math.random(5,10)
-   local inj = math.random(5,10)
-   local ink = math.random(5,10)
+   local ini = math.random(3,5)
+   local inj = math.random(3,5)
+   local ink = math.random(3,5)
    local input = torch.Tensor(ink, inj, ini):zero()
 
    local module = nn.SoftShrink(math.random()/2)
@@ -213,15 +214,15 @@ function nntest.SoftShrink()
 end
 
 function nntest.Power()
-   local in1 = torch.rand(10,20)
+   local in1 = torch.rand(5,7)
    local module = nn.Power(2)
    local out = module:forward(in1)
    local err = out:dist(in1:cmul(in1))
    mytester:asserteq(err, 0, torch.typename(module) .. ' - forward err ')
 
-   local ini = math.random(5,10)
-   local inj = math.random(5,10)
-   local ink = math.random(5,10)
+   local ini = math.random(3,5)
+   local inj = math.random(3,5)
+   local ink = math.random(3,5)
    local pw = torch.uniform()*math.random(1,10)
    local input = torch.Tensor(ink, inj, ini):zero()
 
@@ -236,15 +237,15 @@ function nntest.Power()
 end
 
 function nntest.Square()
-   local in1 = torch.rand(10,20)
+   local in1 = torch.rand(5,7)
    local module = nn.Square()
    local out = module:forward(in1)
    local err = out:dist(in1:cmul(in1))
    mytester:asserteq(err, 0, torch.typename(module) .. ' - forward err ')
 
-   local ini = math.random(5,10)
-   local inj = math.random(5,10)
-   local ink = math.random(5,10)
+   local ini = math.random(3,5)
+   local inj = math.random(3,5)
+   local ink = math.random(3,5)
    local input = torch.Tensor(ink, inj, ini):zero()
 
    local module = nn.Square()
@@ -258,15 +259,15 @@ function nntest.Square()
 end
 
 function nntest.Sqrt()
-   local in1 = torch.rand(10,20)
+   local in1 = torch.rand(5,7)
    local module = nn.Sqrt()
    local out = module:forward(in1)
    local err = out:dist(in1:sqrt())
    mytester:asserteq(err, 0, torch.typename(module) .. ' - forward err ')
 
-   local ini = math.random(5,10)
-   local inj = math.random(5,10)
-   local ink = math.random(5,10)
+   local ini = math.random(3,5)
+   local inj = math.random(3,5)
+   local ink = math.random(3,5)
    local input = torch.Tensor(ink, inj, ini):zero()
 
    local module = nn.Sqrt()
@@ -280,8 +281,8 @@ function nntest.Sqrt()
 end
 
 function nntest.Linear()
-   local ini = math.random(5,7)
-   local inj_vals = {math.random(5,7), 1}  -- Also test the inj = 1 spatial case
+   local ini = math.random(3,5)
+   local inj_vals = {math.random(3,5), 1}  -- Also test the inj = 1 spatial case
    local input = torch.Tensor(ini):zero()
 
    for ind, inj in pairs(inj_vals) do
@@ -350,9 +351,9 @@ function nntest.Linear()
 end
 
 function nntest.SparseLinear()
-   local ini = math.random(5000,10000)
-   local inj = math.random(50,100)
-   local numNonzero = math.random(5,20)
+   local ini = math.random(50,100)
+   local inj = math.random(5,10)
+   local numNonzero = math.random(3,5)
    
    local module = nn.SparseLinear(ini,inj)
 
@@ -412,8 +413,8 @@ function nntest.SparseLinear()
 end
 
 function nntest.Euclidean()
-   local ini = math.random(50,70)
-   local inj = math.random(50,70)
+   local ini = math.random(5,7)
+   local inj = math.random(5,7)
    local input = torch.Tensor(ini):zero()
    local module = nn.Euclidean(ini,inj)
 
@@ -429,8 +430,8 @@ function nntest.Euclidean()
 end
 
 function nntest.WeightedEuclidean()
-   local ini = math.random(10,20)
-   local inj = math.random(10,20)
+   local ini = math.random(3,5)
+   local inj = math.random(13,5)
    local input = torch.Tensor(ini):zero()
    local module = nn.WeightedEuclidean(ini,inj)
 
@@ -475,8 +476,8 @@ local function criterionJacobianTest1D(cri, input, target)
 end
 
 function nntest.MSECriterion()
-   local input = torch.rand(100)
-   local target = input:clone():add(torch.rand(100))
+   local input = torch.rand(10)
+   local target = input:clone():add(torch.rand(10))
    local cri = nn.MSECriterion()
    criterionJacobianTest1D(cri, input, target)   
 end
@@ -489,23 +490,23 @@ function nntest.MarginCriterion()
 end
 
 function nntest.WeightedMSECriterion()
-   local input = torch.rand(100)
-   local target = input:clone():add(torch.rand(100))
-   local cri = nn.WeightedMSECriterion(torch.rand(100))
+   local input = torch.rand(10)
+   local target = input:clone():add(torch.rand(10))
+   local cri = nn.WeightedMSECriterion(torch.rand(10))
    criterionJacobianTest1D(cri, input, target)
 end
 
 function nntest.BCECriterion()
    local eps = 1e-2
-   local input = torch.rand(100)*(1-eps) + eps/2
-   local target = torch.rand(100)*(1-eps) + eps/2
+   local input = torch.rand(10)*(1-eps) + eps/2
+   local target = torch.rand(10)*(1-eps) + eps/2
    local cri = nn.BCECriterion()
    criterionJacobianTest1D(cri, input, target)
 end
 
 function nntest.DistKLDivCriterion()
-   local input = torch.rand(100)
-   local target = input:clone():add(torch.rand(100))
+   local input = torch.rand(10)
+   local target = input:clone():add(torch.rand(10))
    local cri = nn.DistKLDivCriterion(true)  -- sizeAverage = true
    criterionJacobianTest1D(cri, input, target)
    cri = nn.DistKLDivCriterion(false)  -- sizeAverage = false
@@ -529,9 +530,9 @@ function nntest.ClassNLLCriterion()
 end
 
 function nntest.LogSigmoid()
-   local ini = math.random(10,20)
-   local inj = math.random(10,20)
-   local ink = math.random(10,20)
+   local ini = math.random(3,5)
+   local inj = math.random(3,5)
+   local ink = math.random(3,5)
    local input = torch.Tensor(ini,inj,ink):zero()
    local module = nn.LogSigmoid()
 
@@ -544,8 +545,8 @@ function nntest.LogSigmoid()
 end
 
 function nntest.LogSoftmax()
-   local ini = math.random(10,20)
-   local inj = math.random(10,20)
+   local ini = math.random(3,5)
+   local inj = math.random(3,5)
    local input = torch.Tensor(ini,inj):zero()
    local module = nn.LogSoftMax()
 
@@ -572,9 +573,9 @@ end
 -- end
 
 function nntest.Max()
-   local ini = math.random(10,20)
-   local inj = math.random(10,20)
-   local ink = math.random(10,20)
+   local ini = math.random(3,5)
+   local inj = math.random(3,5)
+   local ink = math.random(3,5)
    local input = torch.Tensor(ini,inj*ink):zero()
    local module = nn.Max(1)
 
@@ -587,9 +588,9 @@ function nntest.Max()
 end
 
 function nntest.Min()
-   local ini = math.random(10,20)
-   local inj = math.random(10,20)
-   local ink = math.random(10,20)
+   local ini = math.random(3,5)
+   local inj = math.random(3,5)
+   local ink = math.random(3,5)
    local input = torch.Tensor(ini,inj*ink):zero()
    local module = nn.Min(1)
 
@@ -602,9 +603,9 @@ function nntest.Min()
 end
 
 function nntest.Mean()
-   local ini = math.random(10,20)
-   local inj = math.random(10,20)
-   local ink = math.random(10,20)
+   local ini = math.random(3,5)
+   local inj = math.random(3,5)
+   local ink = math.random(3,5)
    local input = torch.Tensor(ini,inj,ink):zero()
    local module = nn.Mean(torch.random(1,3))
 
@@ -617,9 +618,9 @@ function nntest.Mean()
 end
 
 function nntest.Mul()
-   local ini = math.random(10,20)
-   local inj = math.random(10,20)
-   local ink = math.random(10,20)
+   local ini = math.random(3,5)
+   local inj = math.random(3,5)
+   local ink = math.random(3,5)
    local input = torch.Tensor(ini,inj,ink):zero()
    local module = nn.Mul(ini*inj*ink)
 
@@ -641,9 +642,9 @@ function nntest.Mul()
 end
 
 function nntest.Sigmoid()
-   local ini = math.random(10,20)
-   local inj = math.random(10,20)
-   local ink = math.random(10,20)
+   local ini = math.random(3,5)
+   local inj = math.random(3,5)
+   local ink = math.random(3,5)
    local input = torch.Tensor(ini,inj,ink):zero()
    local module = nn.Sigmoid()
 
@@ -656,8 +657,8 @@ function nntest.Sigmoid()
 end
 
 function nntest.Softmax()
-   local ini = math.random(10,20)
-   local ink = math.random(10,20)
+   local ini = math.random(3,5)
+   local ink = math.random(3,5)
    local input = torch.Tensor(ink, ini):zero()
    local module = nn.SoftMax()
 
@@ -670,8 +671,8 @@ function nntest.Softmax()
 end
 
 function nntest.Softmin()
-   local ini = math.random(10,20)
-   local ink = math.random(10,20)
+   local ini = math.random(3,5)
+   local ink = math.random(3,5)
    local input = torch.Tensor(ink, ini):zero()
    local module = nn.SoftMin()
 
@@ -684,8 +685,8 @@ function nntest.Softmin()
 end
 
 function nntest.Softsign()
-   local ini = math.random(10,20)
-   local ink = math.random(10,20)
+   local ini = math.random(3,5)
+   local ink = math.random(3,5)
    local input = torch.Tensor(ink, ini):zero()
    local module = nn.SoftSign()
 
@@ -698,9 +699,9 @@ function nntest.Softsign()
 end
 
 function nntest.SoftPlus()
-   local ini = math.random(10,20)
-   local inj = math.random(10,20)
-   local ink = math.random(10,20)
+   local ini = math.random(3,5)
+   local inj = math.random(3,5)
+   local ink = math.random(3,5)
    local input = torch.Tensor(ini,inj,ink):zero()
    local module = nn.SoftPlus()
 
@@ -713,9 +714,9 @@ function nntest.SoftPlus()
 end
 
 function nntest.SpatialSubtractiveNormalization_2dkernel()
-   local inputSize = math.random(11,20)
-   local kersize = 9
-   local nbfeatures = math.random(5,10)
+   local inputSize = math.random(6,9)
+   local kersize = 3
+   local nbfeatures = math.random(3,5)
    local kernel = torch.Tensor(kersize,kersize):fill(1)
    local module = nn.SpatialSubtractiveNormalization(nbfeatures,kernel)
    local input = torch.rand(nbfeatures,inputSize,inputSize)
@@ -729,9 +730,9 @@ function nntest.SpatialSubtractiveNormalization_2dkernel()
 end
 
 function nntest.SpatialSubtractiveNormalization_1dkernel()
-   local inputSize = math.random(11,20)
-   local kersize = 9
-   local nbfeatures = math.random(5,10)
+   local inputSize = math.random(6,9)
+   local kersize = 3
+   local nbfeatures = math.random(3,5)
    local kernel = torch.Tensor(kersize):fill(1)
    local module = nn.SpatialSubtractiveNormalization(nbfeatures,kernel)
    local input = torch.rand(nbfeatures,inputSize,inputSize)
@@ -745,9 +746,9 @@ function nntest.SpatialSubtractiveNormalization_1dkernel()
 end
 
 function nntest.SpatialDivisiveNormalization_2dkernel()
-   local inputSize = math.random(11,20)
-   local kersize = 9
-   local nbfeatures = math.random(5,10)
+   local inputSize = math.random(6,9)
+   local kersize = 3
+   local nbfeatures = math.random(3,5)
    local kernel = torch.Tensor(kersize,kersize):fill(1)
    local module = nn.SpatialDivisiveNormalization(nbfeatures,kernel)
    local input = torch.rand(nbfeatures,inputSize,inputSize)
@@ -761,9 +762,9 @@ function nntest.SpatialDivisiveNormalization_2dkernel()
 end
 
 function nntest.SpatialDivisiveNormalization_1dkernel()
-   local inputSize = math.random(11,20)
-   local kersize = 9
-   local nbfeatures = math.random(5,10)
+   local inputSize = math.random(6,9)
+   local kersize = 3
+   local nbfeatures = math.random(3,5)
    local kernel = torch.Tensor(kersize):fill(1)
    local module = nn.SpatialDivisiveNormalization(nbfeatures,kernel)
    local input = torch.rand(nbfeatures,inputSize,inputSize)
@@ -777,14 +778,14 @@ function nntest.SpatialDivisiveNormalization_1dkernel()
 end
 
 function nntest.SpatialConvolution()
-   local from = math.random(1,10)
-   local to = math.random(1,10)
-   local ki = math.random(1,10)
-   local kj = math.random(1,10)
+   local from = math.random(1,5)
+   local to = math.random(1,5)
+   local ki = math.random(1,5)
+   local kj = math.random(1,5)
    local si = math.random(1,4)
    local sj = math.random(1,4)
-   local outi = math.random(10,20)
-   local outj = math.random(10,20)
+   local outi = math.random(5,7)
+   local outj = math.random(5,7)
    local ini = (outi-1)*si+ki
    local inj = (outj-1)*sj+kj
    local module = nn.SpatialConvolution(from, to, ki, kj, si, sj)
@@ -863,34 +864,31 @@ function nntest.SpatialConvolution()
 end
 
 function nntest.SpatialConvolutionMM()
-   local from = math.random(2,10)
-   local to = math.random(1,10)
-   local ki = math.random(1,5)
-   local kj = math.random(1,5)
-   local di = 1 -- NOTE: dw/dh still not supported
-   local dj = 1
-   local padding = math.random(0,2)
-   local outi = math.random(10,20)
-   local outj = math.random(10,20)
-   local ini = outi-padding*2-1+ki
-   local inj = outj-padding*2-1+kj
-   local module = nn.SpatialConvolutionMM(from, to, ki, kj, di, dj, padding)
+   local from = math.random(1,5)
+   local to = math.random(1,5)
+   local ki = math.random(1,3)
+   local kj = math.random(1,3)
+   local outi = math.random(5,9)
+   local outj = math.random(5,9)
+   local ini = outi-1+ki
+   local inj = outj-1+kj
+   local module = nn.SpatialConvolutionMM(from, to, ki, kj)
    local input = torch.Tensor(from, inj, ini):zero()
 
    -- stochastic
-
+   
    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)
    mytester:assertlt(err , precision, 'error on bias ')
 
    local err = jac.testJacobianUpdateParameters(module, input, module.weight)
    mytester:assertlt(err , precision, 'error on weight [direct update] ')
-
+   
    local err = jac.testJacobianUpdateParameters(module, input, module.bias)
    mytester:assertlt(err , precision, 'error on bias [direct update] ')
 
@@ -905,24 +903,28 @@ function nntest.SpatialConvolutionMM()
    end
 
    -- batch
-
+   
    --verbose = true
-   local batch = math.random(2,6)
-   module = nn.SpatialConvolutionMM(from, to, ki, kj, di, dj, padding)
+   local batch = math.random(2,5)
+   outi = math.random(4,8)
+   outj = math.random(4,8)
+   ini = outi-1+ki
+   inj = outj-1+kj
+   module = nn.SpatialConvolutionMM(from, to, ki, kj)
    input = torch.Tensor(batch,from,inj,ini):zero()
 
    local err = jac.testJacobian(module, input)
    mytester:assertlt(err, precision, 'batch error on state ')
-
+   
    local err = jac.testJacobianParameters(module, input, module.weight, module.gradWeight)
    mytester:assertlt(err , precision, 'batch error on weight ')
-
+   
    local err = jac.testJacobianParameters(module, input, module.bias, module.gradBias)
    mytester:assertlt(err , precision, 'batch error on bias ')
 
    local err = jac.testJacobianUpdateParameters(module, input, module.weight)
    mytester:assertlt(err , precision, 'batch error on weight [direct update] ')
-
+   
    local err = jac.testJacobianUpdateParameters(module, input, module.bias)
    mytester:assertlt(err , precision, 'batch error on bias [direct update] ')
 
@@ -935,22 +937,22 @@ function nntest.SpatialConvolutionMM()
       mytester:assertlt(err, precision, string.format(
                          'batch error on bias [%s]', t))
    end
-
+   
    local ferr, berr = jac.testIO(module, input)
    mytester:asserteq(0, ferr, torch.typename(module) .. ' - i/o forward err ')
    mytester:asserteq(0, berr, torch.typename(module) .. ' - i/o backward err ')
 end
 
 function nntest.SpatialConvolutionMap()
-   local from = math.random(1,10)
+   local from = math.random(1,5)
    local fanin = math.random(1, from)
-   local to = math.random(1,10)
-   local ki = math.random(1,10)
-   local kj = math.random(1,10)
-   local si = math.random(1,4)
-   local sj = math.random(1,4)
-   local outi = math.random(10,20)
-   local outj = math.random(10,20)
+   local to = math.random(1,5)
+   local ki = math.random(1,5)
+   local kj = math.random(1,5)
+   local si = math.random(1,3)
+   local sj = math.random(1,3)
+   local outi = math.random(5,9)
+   local outj = math.random(5,9)
    local ini = (outi-1)*si+ki
    local inj = (outj-1)*sj+kj
 
@@ -983,14 +985,14 @@ end
 
 
 function nntest.SpatialFullConvolution()
-   local from = math.random(1,10)
-   local to = math.random(1,10)
-   local ki = math.random(1,10)
-   local kj = math.random(1,10)
+   local from = math.random(1,5)
+   local to = math.random(1,5)
+   local ki = math.random(1,5)
+   local kj = math.random(1,5)
    local si = math.random(1,4)
    local sj = math.random(1,4)
-   local ini = math.random(10,18)
-   local inj = math.random(10,18)
+   local ini = math.random(5,8)
+   local inj = math.random(5,8)
    local module = nn.SpatialFullConvolution(from, to, ki, kj, si, sj)
    local input = torch.Tensor(from, inj, ini):zero()
    
@@ -1058,16 +1060,16 @@ function nntest.SpatialFullConvolution()
 end
 
 function nntest.SpatialFullConvolutionMap()
-   local from = math.ceil(torch.uniform(2,5))
-   local to = math.ceil(torch.uniform(2,7))
+   local from = math.ceil(torch.uniform(2,4))
+   local to = math.ceil(torch.uniform(2,5))
    local fanin = math.ceil(torch.uniform(1, from))
    local tt = nn.tables.random(from, to, fanin)
-   local ki = math.ceil(torch.uniform(2,7))
-   local kj = math.ceil(torch.uniform(2,7))
+   local ki = math.ceil(torch.uniform(2,5))
+   local kj = math.ceil(torch.uniform(2,5))
    local si = math.ceil(torch.uniform(1,3))
    local sj = math.ceil(torch.uniform(1,3))
-   local ini = math.ceil(torch.uniform(10,18))
-   local inj = math.ceil(torch.uniform(10,18))
+   local ini = math.ceil(torch.uniform(5,7))
+   local inj = math.ceil(torch.uniform(5,7))
    local module = nn.SpatialFullConvolutionMap(tt, ki, kj, si, sj)
    local input = torch.Tensor(from, inj, ini):zero()
    
@@ -1103,15 +1105,15 @@ function nntest.SpatialFullConvolutionMap()
 end
 
 function nntest.SpatialFullConvolutionCompare()
-    local from = math.ceil(torch.uniform(2,5))
-    local to = math.ceil(torch.uniform(2,7))
+    local from = math.ceil(torch.uniform(2,4))
+    local to = math.ceil(torch.uniform(2,5))
     local tt = nn.tables.full(from, to)
-    local ki = math.ceil(torch.uniform(2,7))
-    local kj = math.ceil(torch.uniform(2,7))
+    local ki = math.ceil(torch.uniform(2,5))
+    local kj = math.ceil(torch.uniform(2,5))
     local si = math.ceil(torch.uniform(1,3))
     local sj = math.ceil(torch.uniform(1,3))
-    local ini = math.ceil(torch.uniform(10,18))
-    local inj = math.ceil(torch.uniform(10,18))
+    local ini = math.ceil(torch.uniform(7,8))
+    local inj = math.ceil(torch.uniform(7,8))
     local module1 = nn.SpatialFullConvolutionMap(tt, ki, kj, si, sj)
     local module2 = nn.SpatialFullConvolution(from, to, ki, kj, si, sj)
     local input = torch.rand(from, inj, ini)
@@ -1171,14 +1173,14 @@ local function batchcompare(smod, sin, plist)
 end
 
 function nntest.SpatialConvolutionBatchCompare()
-   local from = math.random(1,10)
-   local to = math.random(1,10)
-   local ki = math.random(1,10)
-   local kj = math.random(1,10)
+   local from = math.random(1,5)
+   local to = math.random(1,5)
+   local ki = math.random(1,5)
+   local kj = math.random(1,5)
    local si = math.random(1,4)
    local sj = math.random(1,4)
-   local outi = math.random(10,20)
-   local outj = math.random(10,20)
+   local outi = math.random(5,9)
+   local outj = math.random(5,9)
    local ini = (outi-1)*si+ki
    local inj = (outj-1)*sj+kj
 
@@ -1189,14 +1191,14 @@ function nntest.SpatialConvolutionBatchCompare()
 end
 
 function nntest.SpatialFullConvolutionBatchCompare()
-   local from = math.random(1,10)
-   local to = math.random(1,10)
-   local ki = math.random(1,10)
-   local kj = math.random(1,10)
+   local from = math.random(1,5)
+   local to = math.random(1,5)
+   local ki = math.random(1,5)
+   local kj = math.random(1,5)
    local si = math.random(1,4)
    local sj = math.random(1,4)
-   local ini = math.random(10,18)
-   local inj = math.random(10,18)
+   local ini = math.random(5,9)
+   local inj = math.random(5,9)
 
    local module = nn.SpatialFullConvolution(from, to, ki, kj, si, sj)
    local input = torch.randn(from, inj, ini)
@@ -1207,13 +1209,13 @@ end
 
 
 function nntest.SpatialSubSamplingBatchCompare()
-   local from = math.random(1,10)
+   local from = math.random(1,6)
    local ki = math.random(1,5)
    local kj = math.random(1,5)
    local si = math.random(1,4)
    local sj = math.random(1,4)
-   local outi = math.random(10,20)
-   local outj = math.random(10,20)
+   local outi = math.random(6,10)
+   local outj = math.random(6,10)
    local ini = (outi-1)*si+ki
    local inj = (outj-1)*sj+kj
    local module = nn.SpatialSubSampling(from, ki, kj, si, sj)
@@ -1223,13 +1225,13 @@ function nntest.SpatialSubSamplingBatchCompare()
 end
 
 function nntest.SpatialSubSampling()
-   local from = math.random(1,10)
+   local from = math.random(1,6)
    local ki = math.random(1,5)
    local kj = math.random(1,5)
    local si = math.random(1,4)
    local sj = math.random(1,4)
-   local outi = math.random(10,20)
-   local outj = math.random(10,20)
+   local outi = math.random(6,10)
+   local outj = math.random(6,10)
    local ini = (outi-1)*si+ki
    local inj = (outj-1)*sj+kj
    local module = nn.SpatialSubSampling(from, ki, kj, si, sj)
@@ -1307,10 +1309,10 @@ function nntest.SpatialMaxPooling()
    local from = math.random(1,5)
    local ki = math.random(1,5)
    local kj = math.random(1,5)
-   local si = math.random(1,4)
-   local sj = math.random(1,4)
-   local outi = math.random(10,20)
-   local outj = math.random(10,20)
+   local si = math.random(1,3)
+   local sj = math.random(1,3)
+   local outi = math.random(2,4)
+   local outj = math.random(2,4)
    local ini = (outi-1)*si+ki
    local inj = (outj-1)*sj+kj
 
@@ -1343,8 +1345,8 @@ function nntest.SpatialLPPooling()
    local osizex = math.random(1,4)
    local osizey = math.random(1,4)
    local p = 2
-   local mx = math.random(2,8)
-   local my = math.random(2,8)
+   local mx = math.random(2,6)
+   local my = math.random(2,6)
    local dx = math.random(2,mx)
    local dy = math.random(2,my)
    local sizex = osizex*mx
@@ -1361,9 +1363,9 @@ function nntest.SpatialLPPooling()
 end
 
 function nntest.Sum()
-   local ini = math.random(10,20)
-   local inj = math.random(10,20)
-   local ink = math.random(10,20)
+   local ini = math.random(3,5)
+   local inj = math.random(3,5)
+   local ink = math.random(3,5)
    local input = torch.Tensor(ini,inj,ink):zero()
    local module = nn.Sum(torch.random(1,3))
 
@@ -1376,9 +1378,9 @@ function nntest.Sum()
 end
 
 function nntest.Tanh()
-   local ini = math.random(5,10)
-   local inj = math.random(5,10)
-   local ink = math.random(5,10)
+   local ini = math.random(3,5)
+   local inj = math.random(3,5)
+   local ink = math.random(3,5)
    local input = torch.Tensor(ink, inj, ini):zero()
    
    local module = nn.Tanh()
@@ -1393,11 +1395,11 @@ end
 
 function nntest.TemporalConvolution()
    -- 1D
-   local from = math.random(1,10)
-   local to = math.random(1,10)
-   local ki = math.random(1,10)
+   local from = math.random(1,5)
+   local to = math.random(1,5)
+   local ki = math.random(1,5)
    local si = math.random(1,4)
-   local outi = math.random(10,20)
+   local outi = math.random(5,7)
    local ini = (outi-1)*si+ki
    local module = nn.TemporalConvolution(from, to, ki,si)
    local input = torch.Tensor(ini, from):zero()
@@ -1428,7 +1430,7 @@ function nntest.TemporalConvolution()
    end
    
    -- 2D
-   local nBatchFrame = 8
+   local nBatchFrame = 4
    local input = torch.Tensor(nBatchFrame, ini, from):zero()
    
    local err = jac.testJacobian(module, input)
@@ -1476,9 +1478,9 @@ end
 
 function nntest.TemporalSubSampling()
    local from = math.random(1,5)
-   local ki = math.random(1,10)
+   local ki = math.random(1,6)
    local si = math.random(1,4)
-   local outi = math.random(10,20)
+   local outi = math.random(6,9)
    local ini = (outi-1)*si+ki
    local module = nn.TemporalSubSampling(from, ki, si)
    local input = torch.Tensor(ini, from):zero()
@@ -1514,10 +1516,10 @@ function nntest.TemporalSubSampling()
 end
 
 function nntest.TemporalMaxPooling()
-   local from = math.random(10,10)
-   local ki = math.random(5,10)
+   local from = math.random(2,4)
+   local ki = math.random(5,7)
    local si = math.random(1,2)
-   local outi = math.random(50,90)
+   local outi = math.random(30,40)
    local ini = (outi-1)*si+ki
    local module = nn.TemporalMaxPooling(ki, si)
    local input = torch.Tensor(ini, from):zero()
@@ -1555,17 +1557,17 @@ function nntest.TemporalMaxPooling()
 end
 
 function nntest.VolumetricConvolution()
-   local from = math.random(2,5)
-   local to = math.random(2,5)
-   local kt = math.random(3,7)
-   local ki = math.random(3,7)
-   local kj = math.random(3,7)
-   local st = math.random(2,4)
-   local si = math.random(2,4)
-   local sj = math.random(2,4)
-   local outt = math.random(3,7)
-   local outi = math.random(3,7)
-   local outj = math.random(3,7)
+   local from = math.random(2,3)
+   local to = math.random(2,3)
+   local kt = math.random(3,4)
+   local ki = math.random(3,4)
+   local kj = math.random(3,4)
+   local st = math.random(2,3)
+   local si = math.random(2,3)
+   local sj = math.random(2,3)
+   local outt = math.random(3,4)
+   local outi = math.random(3,4)
+   local outj = math.random(3,4)
    local int = (outt-1)*st+kt
    local ini = (outi-1)*si+ki
    local inj = (outj-1)*sj+kj
@@ -1603,17 +1605,17 @@ function nntest.VolumetricConvolution()
 end
 
 function nntest.VolumetricMaxPooling()
-   local from = math.random(2,5)
+   local from = math.random(2,3)
    local to = from
-   local kt = math.random(3,7)
-   local ki = math.random(3,7)
-   local kj = math.random(3,7)
-   local st = math.random(2,4)
-   local si = math.random(2,4)
-   local sj = math.random(2,4)
-   local outt = math.random(3,7)
-   local outi = math.random(3,7)
-   local outj = math.random(3,7)
+   local kt = math.random(3,4)
+   local ki = math.random(3,4)
+   local kj = math.random(3,4)
+   local st = math.random(2,3)
+   local si = math.random(2,3)
+   local sj = math.random(2,3)
+   local outt = math.random(3,4)
+   local outi = math.random(3,4)
+   local outj = math.random(3,4)
    local int = (outt-1)*st+kt
    local ini = (outi-1)*si+ki
    local inj = (outj-1)*sj+kj
@@ -1806,7 +1808,7 @@ function nntest.PairwiseDistance()
    -- test will break.
    for p = 1,4 do  -- test a few Lp norms
       -- TEST CASE 1: non-batch input, same code path but includes a resize
-      local ini = math.random(10,20)
+      local ini = math.random(3,5)
       local input = torch.Tensor(2, ini):zero()
       local module = nn.Sequential()
       module:add(nn.SplitTable(1))
@@ -1826,7 +1828,7 @@ function nntest.PairwiseDistance()
       mytester:assertlt(err,precision, ' error on non-batch fprop ') 
  
       -- TEST CASE 2: batch input
-      local inj = math.random(10,20)
+      local inj = math.random(3,5)
       input = torch.Tensor(2, inj, ini):zero()
 
       -- (Rebuild the module to avoid correlated tests)
@@ -1854,9 +1856,9 @@ function nntest.PairwiseDistance()
 end
 
 function nntest.LookupTable()
-   local totalIndex = math.random(10,100)
-   local nIndex = math.random(5,7)
-   local entry_size = math.random(5,7)
+   local totalIndex = math.random(6,9)
+   local nIndex = math.random(3,5)
+   local entry_size = math.random(2,5)
    local input = torch.IntTensor(nIndex):zero()
    local module = nn.LookupTable(totalIndex, entry_size)
    local minval = 1
@@ -1876,7 +1878,7 @@ function nntest.LookupTable()
    end
 
    -- 2D
-   local nframe = math.random(50,70)
+   local nframe = math.random(2,5)
    local input = torch.IntTensor(nframe, nIndex):zero()
 
    local err = jac.testJacobianParameters(module, input, module.weight, module.gradWeight, minval, maxval)
@@ -1908,8 +1910,8 @@ end
 function nntest.AddConstant()
   local nbatch = torch.random(3, 5)
   local f = torch.random(3, 5)
-  local h = torch.random(10,20)
-  local w = torch.random(10,20)
+  local h = torch.random(7,9)
+  local w = torch.random(7,9)
   local input = torch.rand(nbatch, f, h, w):mul(20):add(-10)  -- [-10, 10]
 
   local constant = torch.randn(1):squeeze()
@@ -1928,8 +1930,8 @@ end
 function nntest.MulConstant()
   local nbatch = torch.random(3, 5)
   local f = torch.random(3, 5)
-  local h = torch.random(10,20)
-  local w = torch.random(10,20)
+  local h = torch.random(7,9)
+  local w = torch.random(7,9)
   local input = torch.rand(nbatch, f, h, w):mul(20):add(-10)  -- [-10, 10]
 
   local constant = torch.randn(1):squeeze()
@@ -2218,7 +2220,7 @@ end
 
 function nntest.ConcatTable()
    -- Test tensor input
-   local input = torch.rand(10, 10, 10)
+   local input = torch.rand(5, 5, 5)
    local m = nn.Sequential()
    
    local concat = nn.ConcatTable()