diff options
Diffstat (limited to 'test/test.lua')
| -rw-r--r-- | test/test.lua | 32 |
1 files changed, 32 insertions, 0 deletions
diff --git a/test/test.lua b/test/test.lua index ffe6cc3..6ce836f 100644 --- a/test/test.lua +++ b/test/test.lua @@ -128,6 +128,10 @@ function cudnntest.SpatialConvolution() local ini = (outi-1)*si+ki local inj = (outj-1)*sj+kj local scale = math.random() +--for half, the total filter dim has to be even + if testparams.test_type=='torch.CudaHalfTensor' and ki*kj*from*to % 2 == 1 then + to = to+1 + end local input = torch.randn(bs,from,inj,ini):cuda() local gradOutput = torch.randn(bs,to,outj,outi):cuda() @@ -164,6 +168,10 @@ function cudnntest.SpatialDilatedConvolution() local inj = (outj-1)*sj+wj local scale = math.random() +--for half, the total filter dim has to be even + if testparams.test_type=='torch.CudaHalfTensor' and ki*kj*from*to % 2 == 1 then + to = to+1 + end local input = torch.randn(bs,from,inj,ini) local gradOutput = torch.randn(bs,to,outj,outi) @@ -207,6 +215,10 @@ function cudnntest.VolumetricDilatedConvolution() local scale = math.random() +--for half, the total filter dim has to be even + if testparams.test_type=='torch.CudaHalfTensor' and ki*kj*kk*from*to % 2 == 1 then + to = to+1 + end local input = torch.randn(bs,from,ini,ink,inj) local gradOutput = torch.randn(bs,to,outi,outk,outj) local sconv = nn.VolumetricDilatedConvolution(from,to,ki,kj,kk,si,sj,sk,0,0,0,di,dj,dk) @@ -237,6 +249,10 @@ function cudnntest.SpatialFullConvolution() local outi = (ini-1)*si+ki local outj = (inj-1)*sj+kj local scale = math.random() +--for half, the total filter dim has to be even + if testparams.test_type=='torch.CudaHalfTensor' and ki*kj*from*to % 2 == 1 then + to = to+1 + end local input = torch.randn(bs,from,inj,ini):cuda() local gradOutput = torch.randn(bs,to,outj,outi):cuda() @@ -264,6 +280,10 @@ function cudnntest.TemporalConvolution() local outi = math.random(1,15) local ini = (outi - 1) * si + ki local scale = math.random() +--for half, the total filter dim has to be even + if testparams.test_type=='torch.CudaHalfTensor' and inputFrameSize*outputFrameSize*ki % 2 == 1 then + outputFrameSize = outputFrameSize + 1 + end local input = torch.randn(bs,ini,inputFrameSize):cuda() local gradOutput = torch.randn(bs,outi,outputFrameSize):cuda() @@ -288,6 +308,9 @@ function cudnntest.TemporalConvolution_padding_batch() local ini = (outi-1)*si+ki local scale = math.random() + if testparams.test_type=='torch.CudaHalfTensor' and inputFrameSize*outputFrameSize*ki % 2 == 1 then + outputFrameSize = outputFrameSize + 1 + end local inputpadded = torch.randn(bs,ini,inputFrameSize):cuda() for i=1,pad_h do inputpadded:narrow(2,i,1):fill(0) @@ -340,6 +363,9 @@ function cudnntest.TemporalConvolution_reduceBatchSize() local ini = (outi-1)*si+ki local batchSize = 128 local smallerBatchSize = batchSize/2 + if testparams.test_type=='torch.CudaHalfTensor' and inputFrameSize*outputFrameSize*ki % 2 == 1 then + outputFrameSize = outputFrameSize + 1 + end local input = cast(torch.randn(batchSize,ini,inputFrameSize)) local conv = cast(cudnn.TemporalConvolution(inputFrameSize,outputFrameSize,ki,si):cuda()) @@ -372,6 +398,9 @@ function cudnntest.VolumetricConvolution() local inj = outj*sj+kj-1 local ink = outk*sk+kk-1 + if testparams.test_type=='torch.CudaHalfTensor' and ki*kj*kk*from*to % 2 == 1 then + to = to+1 + end local scale = math.random() local input = torch.randn(bs,from,ink,inj,ini):cuda() @@ -408,6 +437,9 @@ function cudnntest.VolumetricFullConvolution() local outj = (inj-1)*sj+kj local outk = (ink-1)*sk+kk local scale = math.random() + if testparams.test_type=='torch.CudaHalfTensor' and ki*kj*kk*from*to % 2 == 1 then + to = to+1 + end if testparams.test_type == 'torch.CudaDoubleTensor' then return |