Merge pull request #300 from fmassa/cosinedist_batch

CosineDistance supports batch mode

1 files changed, 50 insertions, 0 deletions

diff --git a/test.lua b/test.lua
index 3f37dac..cd48433 100644
--- a/test.lua
+++ b/test.lua
@@ -3724,6 +3724,56 @@ function nntest.BatchMMTransposeBoth()
   end
 end
 
+function nntest.CosineDistance()
+  local indim = math.random(1,10)
+  local input = {torch.rand(indim),torch.rand(indim)}
+
+  -- check forward against previous implementation
+  local module = nn.CosineDistance()
+
+  local w1 = input[1]:dot(input[2])
+  local w2 = math.sqrt(input[1]:dot(input[1]))
+  local w3 = math.sqrt(input[2]:dot(input[2]))
+  local output_old = w1/w2/w3
+
+  local output = module:forward(input)
+
+  mytester:assertlt(math.abs(output_old-output[1]),precision,'error on forward ')
+
+
+  -- check gradients
+  -- 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.
+  local input = torch.rand(2,indim)
+  local module = nn.Sequential()
+  module:add(nn.SplitTable(1))
+  module:add(nn.CosineDistance())
+
+  local err = jac.testJacobian(module,input)
+  mytester:assertlt(err,precision, 'error on state ')
+
+  -- IO
+  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 ')
+
+  -- batch
+  -- rebuild module to avoid correlated tests
+  local module = nn.Sequential()
+  module:add(nn.SplitTable(1))
+  module:add(nn.CosineDistance())
+
+  local nframes = math.random(1,10)
+  local indim = math.random(1,10)
+  local input = torch.rand(2,nframes,indim)
+
+  local err = jac.testJacobian(module,input)
+  mytester:assertlt(err,precision, 'batch error on state ')
+ 
+end
+
 function nntest.CosineEmbeddingCriterion()
   local v1 = torch.Tensor{1, 0}
   local v2 = torch.Tensor{0.5, math.sqrt(3)*0.5}