functional interface for R3 as well

1 files changed, 54 insertions, 3 deletions

diff --git a/test/test.lua b/test/test.lua
index c2938de..4062425 100644
--- a/test/test.lua
+++ b/test/test.lua
@@ -724,7 +724,7 @@ function cudnntest.LogSoftMax_batch()
                      precision_backward, 'error on state (backward) ')
 end
 
-function cudnntest.functional_SpatialBias()
+function cudnntest.functional_bias2D()
    local bs = math.random(1,32)
    local from = math.random(1,32)
    local to = math.random(1,64)
@@ -742,7 +742,7 @@ function cudnntest.functional_SpatialBias()
    mod.weight:zero()
    local groundtruth = mod:forward(input)
    local result = groundtruth:clone():zero()
-   cudnn.functional.SpatialBias_updateOutput(mod.bias, result)
+   cudnn.functional.bias2D_updateOutput(cudnn.getHandle(), mod.bias, result)
    local error = result:float() - groundtruth:float()
    mytester:assertlt(error:abs():max(),
                      precision_forward, 'error on forward ')
@@ -752,12 +752,63 @@ function cudnntest.functional_SpatialBias()
    mod:backward(input, gradOutput, scale)
    local groundtruth = mod.gradBias
    local result = groundtruth:clone():zero()
-   cudnn.functional.SpatialBias_accGradParameters(gradOutput, result, scale)
+   cudnn.functional.bias2D_accGradParameters(cudnn.getHandle(), gradOutput, result, scale)
    error = result:float() - groundtruth:float()
    mytester:assertlt(error:abs():max(),
                      precision_backward, 'error on accGradParameters ')
 end
 
+function cudnntest.functional_convolution2d()
+    local a=cudnn.SpatialConvolution(3,16,5,5):cuda()
+    a.bias:zero();
+    local input = torch.randn(10,3,10,10):cuda()
+    a:zeroGradParameters()
+    a:forward(input);
+    local output = a.output:clone():normal()
+    local gradOutput = a.output:clone():normal()
+    local gradInput = a:backward(input, gradOutput):clone():normal()
+    local gradWeight = a.gradWeight:clone():zero()
+    cudnn.functional.Convolution2D_updateOutput(cudnn.getHandle(), input,
+                                                a.weight, output, a.dH,
+                                                a.dW, a.padH, a.padW)
+    mytester:assertlt((output - a.output):abs():max(),
+                     precision_forward, 'error on forward ')
+
+    cudnn.functional.Convolution2D_updateGradInput(cudnn.getHandle(), input,
+                                                   a.weight, output, gradOutput,
+                                                   gradInput,
+                                                   a.dH, a.dW, a.padH, a.padW)
+    mytester:assertlt((gradInput - a.gradInput):abs():max(),
+                     precision_forward, 'error on updateGradInput ')
+
+    cudnn.functional.Convolution2D_accGradParameters(cudnn.getHandle(), input,
+                                                   gradWeight, gradOutput,
+                                                   a.dH, a.dW, a.padH, a.padW)
+    mytester:assertlt((gradWeight - a.gradWeight):abs():max(),
+                     precision_forward, 'error on accGradParameters ')
+end
+
+function cudnntest.functional_maxpooling2d()
+    local a=cudnn.SpatialMaxPooling(2,2,2,2):cuda()
+    local input = torch.randn(10,3,10,10):cuda()
+    a:forward(input);
+    local output = a.output:clone():normal()
+    local gradOutput = a.output:clone():normal()
+    local gradInput = a:backward(input, gradOutput):clone():normal()
+    cudnn.functional.MaxPooling2D_updateOutput(cudnn.getHandle(), input,
+                                               output, a.kH, a.kW,
+                                               a.dH, a.dW, a.padH, a.padW)
+    mytester:assertlt((output - a.output):abs():max(),
+                     precision_forward, 'error on forward ')
+
+    cudnn.functional.MaxPooling2D_updateGradInput(cudnn.getHandle(), input,
+                                                   output, gradOutput, gradInput,
+                                                   a.kH, a.kW, a.dH, a.dW,
+                                                   a.padH, a.padW)
+    mytester:assertlt((gradInput - a.gradInput):abs():max(),
+                     precision_forward, 'error on updateGradInput ')
+end
+
 
 torch.setdefaulttensortype('torch.FloatTensor')
 math.randomseed(os.time())