ParallelCriterion

1 files changed, 60 insertions, 0 deletions

diff --git a/test.lua b/test.lua
index a28674a..9414a66 100644
--- a/test.lua
+++ b/test.lua
@@ -811,6 +811,66 @@ function nntest.MarginRankingCriterion()
    mytester:assert(torch.type(gradInput2[2]) == 'torch.FloatTensor', "MRC:type() error 2")
 end
 
+function nntest.ParallelCriterion()
+   local input = {torch.rand(2,10), torch.randn(2,10)}
+   local target = {torch.IntTensor{1,8}, torch.randn(2,10)}
+   local nll = nn.ClassNLLCriterion()
+   local mse = nn.MSECriterion()
+   local pc = nn.ParallelCriterion():add(nll, 0.5):add(mse)
+   local output = pc:forward(input, target)
+   local output2 = nll:forward(input[1], target[1])/2 + mse:forward(input[2], target[2])
+   mytester:assert(math.abs(output2 - output) < 0.00001, "ParallelCriterion forward error")
+   local gradInput = pc:backward(input, target)
+   local gradInput2 = {nll:backward(input[1], target[1]):clone():div(2), mse:backward(input[2], target[2])}
+   mytester:assertTensorEq(gradInput[1], gradInput2[1], 0.000001, "ParallelCriterion backward error 1")
+   mytester:assertTensorEq(gradInput[2], gradInput2[2], 0.000001, "ParallelCriterion backward error 2")
+   -- test type
+   pc:float()
+   gradInput[1], gradInput[2] = gradInput[1]:clone(), gradInput[2]:clone()
+   local input3 = {input[1]:float(), input[2]:float()}
+   local target3 = {target[1]:float(), target[2]:float()}
+   local output3 = pc:forward(input3, target3)
+   local gradInput3 = pc:backward(input3, target3)
+   mytester:assert(math.abs(output3 - output) < 0.00001, "ParallelCriterion forward error type")
+   mytester:assertTensorEq(gradInput[1]:float(), gradInput3[1], 0.000001, "ParallelCriterion backward error 1 type")
+   mytester:assertTensorEq(gradInput[2]:float(), gradInput3[2], 0.000001, "ParallelCriterion backward error 2 type")
+   -- test repeatTarget
+   local input = {torch.rand(2,10), torch.randn(2,10)}
+   local target = torch.randn(2,10)
+   local mse = nn.MSECriterion()
+   local pc = nn.ParallelCriterion(true):add(mse, 0.5):add(mse:clone())
+   local output = pc:forward(input, target)
+   local output2 = mse:forward(input[1], target)/2 + mse:forward(input[2], target)
+   mytester:assert(math.abs(output2 - output) < 0.00001, "ParallelCriterion repeatTarget forward error")
+   local gradInput = pc:backward(input, target)
+   local gradInput2 = {mse:backward(input[1], target):clone():div(2), mse:backward(input[2], target)}
+   mytester:assertTensorEq(gradInput[1], gradInput2[1], 0.000001, "ParallelCriterion repeatTarget backward error 1")
+   mytester:assertTensorEq(gradInput[2], gradInput2[2], 0.000001, "ParallelCriterion repeatTarget backward error 2")
+end
+
+function nntest.MultiCriterion()
+   local input = torch.rand(2,10)
+   local target = torch.IntTensor{1,8}
+   local nll = nn.ClassNLLCriterion()
+   local nll2 = nn.CrossEntropyCriterion()
+   local mc = nn.MultiCriterion():add(nll, 0.5):add(nll2)
+   local output = mc:forward(input, target)
+   local output2 = nll:forward(input, target)/2 + nll2:forward(input, target)
+   mytester:assert(math.abs(output2 - output) < 0.00001, "MultiCriterion forward error")
+   local gradInput = mc:backward(input, target)
+   local gradInput2 = nll:backward(input, target):clone():div(2):add(nll2:backward(input, target))
+   mytester:assertTensorEq(gradInput, gradInput2, 0.000001, "MultiCriterion backward error ")
+   -- test type
+   mc:float()
+   gradInput = gradInput:clone()
+   local input3 = input:float()
+   local target3 = target:float()
+   local output3 = mc:forward(input3, target3)
+   local gradInput3 = mc:backward(input3, target3)
+   mytester:assert(math.abs(output3 - output) < 0.00001, "MultiCriterion forward error type")
+   mytester:assertTensorEq(gradInput:float(), gradInput3, 0.000001, "MultiCriterion backward error type")
+end
+
 function nntest.WeightedMSECriterion()
    local input = torch.rand(10)
    local target = input:clone():add(torch.rand(10))