Natalia's fixed for BN. Added bntest.lua

2 files changed, 47 insertions, 0 deletions

diff --git a/test/bntest.lua b/test/bntest.lua
new file mode 100644
index 0000000..8ebd1fa
--- /dev/null
+++ b/test/bntest.lua
@@ -0,0 +1,19 @@
+require 'cunn'
+require 'cudnn'
+
+local h=5
+local w=5
+local bsz=4
+local from=4
+local input = torch.randn(bsz,from,h,w):cuda()
+local gradOutput = torch.randn(bsz,from,h,w):cuda()
+local cbn = cudnn.SpatialBatchNormalization(bsz, 1e-3):cuda()
+local gbn = nn.SpatialBatchNormalization(bsz, 1e-3):cuda()
+local groundtruth = gbn:forward(input)
+local rescuda = cbn:forward(input)
+local resgrad = cbn:backward(input, gradOutput)
+local groundgrad = gbn:backward(input, gradOutput)
+local error = (rescuda:float() - groundtruth:float()):abs():max()
+print("error",error)
+error = (resgrad:float() - groundgrad:float()):abs():max()
+print("error back",error)
diff --git a/test/test.lua b/test/test.lua
index 8c97ffa..9b85499 100644
--- a/test/test.lua
+++ b/test/test.lua
@@ -764,6 +764,34 @@ function cudnntest.SpatialLogSoftMax()
     mytester:assertlt(err, precision_backward, 'error in difference between central difference and :backward')
 end
 
+
+function cudnntest.SpatialBatchNormalization()
+   -- batch   
+   local h = 4 --math.random(5,10)
+   local w = 4 --math.random(5,10)
+   local bsz = 4 --math.random(1, 32)
+   local from = 4 --math.random(1, 32)
+   local input = torch.randn(bsz,from,h,w):cuda()
+   local gradOutput = torch.randn(bsz,from,h,w):cuda()
+   local cbn = cudnn.SpatialBatchNormalization(bsz, 1e-3):cuda()
+   local gbn = nn.SpatialBatchNormalization(bsz, 1e-3):cuda()
+
+   local rescuda = cbn:forward(input)
+   local groundtruth = gbn:forward(input)
+   local resgrad = cbn:backward(input, gradOutput)
+   local groundgrad = gbn:backward(input, gradOutput)
+   
+
+   local error = rescuda:float() - groundtruth:float()
+   mytester:assertlt(error:abs():max(),
+                     precision_forward, 'error in batch normalization (forward) ')
+   error = resgrad:float() - groundgrad:float()
+   mytester:assertlt(error:abs():max(),
+                     precision_backward, 'error in batch normalization (backward) ')
+   
+end
+
+
 function cudnntest.SpatialCrossEntropyCriterion()
     -- batch
     local numLabels = math.random(5,10)