syncing uptil master/R3 9bc3cbac4f054438f5e77824f868cd94e9e22f81

1 files changed, 80 insertions, 4 deletions

diff --git a/test/test.lua b/test/test.lua
index e11f048..e4afe5a 100644
--- a/test/test.lua
+++ b/test/test.lua
@@ -5,9 +5,11 @@ local cudnntest = {}
 local precision_forward = 1e-4
 local precision_backward = 1e-2
 local precision_jac = 1e-3
+local precision_io = 1e-5
 local nloop = 1
 local times = {}
 local mytester
+local jac = nn.Jacobian
 
 function cudnntest.SpatialConvolution_forward_batch()
    local bs = math.random(1,32)
@@ -32,6 +34,11 @@ function cudnntest.SpatialConvolution_forward_batch()
    cutorch.synchronize()
    local error = rescuda:float() - groundtruth:float()
    mytester:assertlt(error:abs():max(), precision_forward, 'error on state (forward) ')
+
+   -- IO
+   local ferr,berr = jac.testIO(gconv, input)
+   mytester:assertlt(ferr, precision_io, torch.typename(gconv) .. ' - i/o forward err ')
+   mytester:assertlt(berr, precision_io, torch.typename(gconv) .. ' - i/o backward err ')
 end
 
 
@@ -175,7 +182,7 @@ function cudnntest.TemporalConvolution_batch()
 
    local input = torch.randn(bs,ini,inputFrameSize):cuda()
    local gradOutput = torch.randn(bs,outi,outputFrameSize):cuda()
-   local sconv = nn.TemporalConvolution(inputFrameSize,outputFrameSize, ki, si):cuda()   
+   local sconv = nn.TemporalConvolution(inputFrameSize,outputFrameSize, ki, si):cuda()
    local groundForward = sconv:forward(input)
    sconv:zeroGradParameters()
    local groundgrad = sconv:backward(input, gradOutput, scale)
@@ -228,7 +235,7 @@ function cudnntest.TemporalConvolution_padding_batch()
    local input = torch.Tensor(bs,ini - 2 * pad_h, inputFrameSize):cuda()
    input:copy(inputpadded:narrow(2, pad_h+1, ini - 2 * pad_h))
    local gradOutput = torch.randn(bs,outi,outputFrameSize):cuda()
-   local sconv = nn.TemporalConvolution(inputFrameSize,outputFrameSize, ki, si):cuda()   
+   local sconv = nn.TemporalConvolution(inputFrameSize,outputFrameSize, ki, si):cuda()
    local groundForward = sconv:forward(inputpadded)
    sconv:zeroGradParameters()
    local groundgrad = sconv:backward(inputpadded, gradOutput, scale)
@@ -338,6 +345,11 @@ function cudnntest.VolumetricConvolution_forward_single()
    local error = rescuda:float() - groundtruth:float()
    mytester:assertlt(error:abs():max(), precision_forward,
                      'error on state (forward) ')
+
+   -- IO
+   local ferr,berr = jac.testIO(gconv, input)
+   mytester:assertlt(ferr, precision_io, torch.typename(gconv) .. ' - i/o forward err ')
+   mytester:assertlt(berr, precision_io, torch.typename(gconv) .. ' - i/o backward err ')
 end
 
 function cudnntest.VolumetricConvolution_backward_single()
@@ -433,6 +445,11 @@ function cudnntest.VolumetricMaxPooling_batch()
    mytester:assertlt(error:abs():max(), precision_forward, 'error on state (forward) ')
    error = resgrad:float() - groundgrad:float()
    mytester:assertlt(error:abs():max(), precision_backward, 'error on state (backward) ')
+
+   -- IO
+   local ferr,berr = jac.testIO(gconv, input)
+   mytester:assertlt(ferr, precision_io, torch.typename(gconv) .. ' - i/o forward err ')
+   mytester:assertlt(berr, precision_io, torch.typename(gconv) .. ' - i/o backward err ')
 end
 
 function cudnntest.VolumetricMaxPooling_single()
@@ -497,7 +514,7 @@ function cudnntest.SpatialMaxPooling_batch()
    local groundgrad = sconv:backward(input, gradOutput)
    cutorch.synchronize()
    local gconv = cudnn.SpatialMaxPooling(ki,kj,si,sj,padi,padj):cuda()
-   if ceil_mode then sconv:ceil() end
+   if ceil_mode then gconv:ceil() end
    local rescuda = gconv:forward(input)
    -- serialize and deserialize
    torch.save('modelTemp.t7', gconv)
@@ -511,6 +528,11 @@ function cudnntest.SpatialMaxPooling_batch()
    mytester:assertlt(error:abs():max(), precision_forward, 'error on state (forward) ')
    error = resgrad:float() - groundgrad:float()
    mytester:assertlt(error:abs():max(), precision_backward, 'error on state (backward) ')
+
+   -- IO
+   local ferr,berr = jac.testIO(gconv, input)
+   mytester:assertlt(ferr, precision_io, torch.typename(gconv) .. ' - i/o forward err ')
+   mytester:assertlt(berr, precision_io, torch.typename(gconv) .. ' - i/o backward err ')
 end
 
 function cudnntest.SpatialMaxPooling_single()
@@ -535,7 +557,7 @@ function cudnntest.SpatialMaxPooling_single()
    local groundgrad = sconv:backward(input, gradOutput)
    cutorch.synchronize()
    local gconv = cudnn.SpatialMaxPooling(ki,kj,si,sj,padi,padj):cuda()
-   if ceil_mode then sconv:ceil() end
+   if ceil_mode then gconv:ceil() end
    local _ = gconv:forward(input)
    -- serialize and deserialize
    torch.save('modelTemp.t7', gconv)
@@ -585,6 +607,11 @@ function cudnntest.SpatialAveragePooling_batch()
    mytester:assertlt(error:abs():max(), precision_forward, 'error on state (forward) ')
    error = resgrad:float() - groundgrad:float()
    mytester:assertlt(error:abs():max(), precision_backward, 'error on state (backward) ')
+
+   -- IO
+   local ferr,berr = jac.testIO(gconv, input)
+   mytester:assertlt(ferr, precision_io, torch.typename(gconv) .. ' - i/o forward err ')
+   mytester:assertlt(berr, precision_io, torch.typename(gconv) .. ' - i/o backward err ')
 end
 
 function cudnntest.SpatialAveragePooling_single()
@@ -729,6 +756,45 @@ function cudnntest.Sigmoid_batch()
    nonlinBatch('Sigmoid')
 end
 
+function cudnntest.SpatialCrossMapLRN_batch()
+   local bs = math.random(4,10)
+   local inputSize = math.random(6,9)
+   local size = math.random(1,3)*2+1
+   local nbfeatures = math.random(3,8)
+   local alpha = math.random(1,100)/100
+   local beta  = math.random(0,100)/100
+   local k = math.random(1,3)
+
+   local tm = {}
+   local title = string.format('SpatialCrossMapLRN.forward')
+   times[title] = tm
+
+   local input = torch.rand(bs, nbfeatures, inputSize, inputSize):cuda()
+   local gradOutput = torch.rand(input:size()):cuda()
+   local sconv = nn.SpatialCrossMapLRN(size, alpha, beta, k):cuda()
+   local gconv = cudnn.SpatialCrossMapLRN(size, alpha, beta, k):cuda()
+
+   local groundtruth = sconv:forward(input):clone()
+   local groundgrad = sconv:backward(input, gradOutput)
+   cutorch.synchronize()
+   gconv:forward(input)
+   -- serialize and deserialize
+   torch.save('modelTemp.t7', gconv)
+   gconv = torch.load('modelTemp.t7')
+   local rescuda = gconv:forward(input)
+   local resgrad = gconv:backward(input, gradOutput)
+   cutorch.synchronize()
+   mytester:asserteq(rescuda:dim(), 4, 'error in dimension')
+   mytester:asserteq(resgrad:dim(), 4, 'error in dimension')
+   local error = rescuda:float() - groundtruth:float()
+   mytester:assertlt(error:abs():max(), precision_forward,
+                     'error on state (forward) ')
+   error = resgrad:float() - groundgrad:float()
+   mytester:assertlt(error:abs():max(), precision_backward,
+                     'error on state (backward) ')
+end
+
+
 function cudnntest.SoftMax_single()
    local sz = math.random(1,64)
    local input = torch.randn(sz):cuda()
@@ -815,6 +881,11 @@ function cudnntest.SoftMax_batch()
    error = resgrad:float() - groundgrad:float()
    mytester:assertlt(error:abs():max(),
                      precision_backward, 'error on state (backward) ')
+
+   -- IO
+   local ferr,berr = jac.testIO(gconv, input)
+   mytester:assertlt(ferr, precision_io, torch.typename(gconv) .. ' - i/o forward err ')
+   mytester:assertlt(berr, precision_io, torch.typename(gconv) .. ' - i/o backward err ')
 end
 
 
@@ -876,6 +947,11 @@ function cudnntest.LogSoftMax_batch()
    error = resgrad:float() - groundgrad:float()
    mytester:assertlt(error:abs():max(),
                      precision_backward, 'error on state (backward) ')
+
+   -- IO
+   local ferr,berr = jac.testIO(gconv, input)
+   mytester:assertlt(ferr, precision_io, torch.typename(gconv) .. ' - i/o forward err ')
+   mytester:assertlt(berr, precision_io, torch.typename(gconv) .. ' - i/o backward err ')
 end
 
 function cudnntest.SpatialLogSoftMax()