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require 'cudnn'
require 'cunn'
local cudnntest = {}
local precision_forward = 1e-4
local precision_backward = 1e-2
local precision_jac = 1e-3
local nloop = 1
local times = {}
local mytester
local function testSpatialFullConv (imageWidth, imageHeight, nPlanesIn, nPlanesOut, kW, kH, dW, dH, padW, padH, adjW, adjH)
print ("Running testSpatialFullConv (" ..
"imageWidth = " .. imageWidth .. ", " ..
"imageHeight = " .. imageHeight .. ", " ..
"nPlanesIn = " .. nPlanesIn .. ", " ..
"nPlanesOut = " .. nPlanesOut .. ", " ..
"kW = " .. kW .. ", " ..
"kH = " .. kH .. ", " ..
"dW = " .. dW .. ", " ..
"dH = " .. dH .. ", " ..
"padW = " .. padW .. ", " ..
"padH = " .. padH .. ", " ..
"adjW = " .. adjW .. ", " ..
"adjH = " .. adjH)
local layerInput = torch.randn(1, nPlanesIn, imageHeight, imageWidth):cuda()
local modelGT = nn.SpatialFullConvolution (nPlanesIn, nPlanesOut, kW, kH, dW, dH, padW, padH, adjW, adjH)
local modelCUDNN = cudnn.SpatialFullConvolution (nPlanesIn, nPlanesOut, kW, kH, dW, dH, padW, padH, adjW, adjH)
modelCUDNN.weight:copy (modelGT.weight)
modelCUDNN.bias:copy (modelGT.bias)
modelGT:cuda()
modelCUDNN:cuda()
local outputGT = modelGT:forward (layerInput)
local outputCUDNN = modelCUDNN:forward (layerInput)
local errorOutput = outputCUDNN:float() - outputGT:float()
mytester:assertlt(errorOutput:abs():max(), precision_forward, 'error on state (forward) ')
-- Now check the backwards diffs
local crit = nn.MSECriterion()
crit:cuda()
local target = outputGT:clone()
target:zero()
target:cuda()
local f = crit:forward (outputGT, target)
local df_do = crit:backward (outputGT, target)
local gradCUDNN = modelCUDNN:updateGradInput (layerInput, df_do)
local gradGT = modelGT:updateGradInput (layerInput, df_do)
local errorGradInput = gradCUDNN:float() - gradGT:float()
mytester:assertlt(errorGradInput:abs():max(), precision_backward, 'error on grad input (backward) ')
modelCUDNN:zeroGradParameters()
modelCUDNN:accGradParameters (layerInput, df_do, 1.0)
modelGT:zeroGradParameters()
modelGT:accGradParameters (layerInput, df_do:cuda(), 1.0)
local errorGradBias = (modelCUDNN.gradBias - modelGT.gradBias)
mytester:assertlt(errorGradBias:abs():max(), precision_backward, 'error on grad bias (backward) ')
local errorGradWeight = (modelCUDNN.gradWeight - modelGT.gradWeight)
mytester:assertlt(errorGradWeight:abs():max(), precision_backward, 'error on grad weight (backward) ')
end
function cudnntest.SpatialConvolution_params()
-- Test with a wide variety of different parameter values:
testSpatialFullConv (5, 5, 1, 1, 3, 3, 2, 2, 0, 0, 0, 0)
testSpatialFullConv (5, 5, 1, 1, 3, 3, 2, 2, 1, 1, 0, 0)
testSpatialFullConv (5, 7, 1, 1, 3, 1, 2, 2, 1, 1, 0, 0)
testSpatialFullConv (7, 5, 1, 1, 3, 1, 1, 1, 1, 1, 0, 0)
testSpatialFullConv (8, 5, 3, 1, 3, 3, 2, 2, 1, 1, 0, 0)
testSpatialFullConv (5, 5, 1, 3, 3, 3, 2, 2, 1, 1, 0, 0)
testSpatialFullConv (5, 5, 5, 3, 3, 3, 2, 2, 1, 1, 1, 1)
testSpatialFullConv (9, 9, 3, 3, 3, 5, 2, 3, 0, 1, 1, 0)
end
torch.setdefaulttensortype('torch.FloatTensor')
math.randomseed(os.time())
mytester = torch.Tester()
mytester:add(cudnntest)
for i=1,cutorch.getDeviceCount() do
print('Running test on device: ' .. i)
cutorch.setDevice(i)
mytester:run(tests)
end
os.execute('rm -f modelTemp.t7')
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