Merge remote-tracking branch 'upstream/master'

1 files changed, 54 insertions, 0 deletions

diff --git a/test/test.lua b/test/test.lua
index 89db059..27bb114 100644
--- a/test/test.lua
+++ b/test/test.lua
@@ -1576,6 +1576,60 @@ function nntest.Module_getParameters_7()
    mytester:asserteq(p:nElement(), 121, 'error: incorrect number of elements in flat vector')
 end
 
+function nntest.PairwiseDistance()
+   -- Note: testJacobian doesn't support table inputs, and rather than re-write
+   -- it so that it does, I'll just use a split table module on the input.
+   -- I assume both SplitTable and Sequential do not have bugs, otherwise this
+   -- test will break.
+   for p = 1,4 do  -- test a few Lp norms
+      -- TEST CASE 1: non-batch input, same code path but includes a resize
+      local ini = math.random(10,20)
+      local input = torch.Tensor(2, ini):zero()
+      local module = nn.Sequential()
+      module:add(nn.SplitTable(1))
+      module:add(nn.PairwiseDistance(p))
+
+      local err = jac.testJacobian(module,input)
+      mytester:assertlt(err,precision, ' error on state ')
+ 
+      local ferr,berr = jac.testIO(module,input)
+      mytester:asserteq(ferr, 0, torch.typename(module)..' - i/o forward err ')
+      mytester:asserteq(berr, 0, torch.typename(module)..' - i/o backward err ')
+
+      -- Also check that the forward prop result is correct.
+      input = torch.rand(2, ini)
+      err = torch.dist(input:select(1,1), input:select(1,2), p) - 
+        module:forward(input)[1]
+      mytester:assertlt(err,precision, ' error on non-batch fprop ') 
+ 
+      -- TEST CASE 2: batch input
+      local inj = math.random(10,20)
+      input = torch.Tensor(2, inj, ini):zero()
+
+      -- (Rebuild the module to avoid correlated tests)
+      module = nn.Sequential()
+      module:add(nn.SplitTable(1))
+      module:add(nn.PairwiseDistance(p))
+
+      err = jac.testJacobian(module,input)
+      mytester:assertlt(err,precision, ' error on state ')
+
+      -- Also check that the forward prop result is correct.
+      -- manually calculate each distance separately
+      local inputa = torch.rand(inj,ini)
+      local inputb = torch.rand(inj,ini)
+      local dist_manual = torch.Tensor(inj)
+      for i=1, inputa:size(1) do
+         dist_manual[i] = torch.dist(inputa:select(1,i), inputb:select(1,i),p) 
+      end
+      -- compare the distances to the module's fprop
+      local dist = module:forward(torch.cat(inputa,inputb,1):resize(2,inj,ini))
+      err = dist - dist_manual 
+      mytester:assertlt(err:norm(), precision, torch.typename(module) .. 
+         ' error on batch fprop ')
+  end
+end
+
 mytester:add(nntest)
 
 if not nn then