1 files changed, 77 insertions, 0 deletions
diff --git a/test/test.lua b/test/test.lua
index 5a127db..5db941a 100644
--- a/test/test.lua
+++ b/test/test.lua
@@ -1789,6 +1789,83 @@ function nntest.LookupTable()
    mytester:asserteq(ferr, 0, torch.typename(module) .. ' - i/o forward err ')
    mytester:asserteq(berr, 0, torch.typename(module) .. ' - i/o backward err ')
 end
+ 
+function nntest.AddConstant()
+  local nbatch = torch.random(3, 5)
+  local f = torch.random(3, 5)
+  local h = torch.random(10,20)
+  local w = torch.random(10,20)
+  local input = torch.rand(nbatch, f, h, w):mul(20):add(-10)  -- [-10, 10]
+
+  local constant = torch.randn(1):squeeze()
+  local mod = nn.AddConstant(constant)
+
+  -- Test FPROP
+  local output = mod:forward(input)
+  local delta = output - input
+  mytester:assertlt(delta:add(-constant):abs():max(), precision, 'fprop error')
+
+  -- Test BPROP
+  local err = jac.testJacobian(mod, input)
+  mytester:assertlt(err, precision, 'bprop error ')
+end
+
+function nntest.MulConstant()
+  local nbatch = torch.random(3, 5)
+  local f = torch.random(3, 5)
+  local h = torch.random(10,20)
+  local w = torch.random(10,20)
+  local input = torch.rand(nbatch, f, h, w):mul(20):add(-10)  -- [-10, 10]
+
+  local constant = torch.randn(1):squeeze()
+  local mod = nn.MulConstant(constant)
+
+  -- Test FPROP
+  local output = mod:forward(input)
+  local scale = output:clone():cdiv(input)
+  mytester:assertlt(scale:add(-constant):abs():max(), precision, 'fprop error')
+
+  -- Test BPROP
+  local err = jac.testJacobian(mod, input)
+  mytester:assertlt(err, precision, 'bprop error ')
+end
+
+function nntest.JoinTable()
+   local tensor = torch.rand(3,4,5)
+   local input = {tensor, tensor}
+   local module
+   for d = 1,tensor:dim() do
+      module = nn.JoinTable(d)
+      mytester:asserteq(module:forward(input):size(d), tensor:size(d)*2, "dimension " .. d)
+   end
+
+   -- Minibatch
+   local tensor = torch.rand(3,4,5)
+   local input = {tensor, tensor}
+   local module
+   for d = 1,tensor:dim()-1 do
+      module = nn.JoinTable(d, 2)
+      mytester:asserteq(module:forward(input):size(d+1), tensor:size(d+1)*2, "dimension " .. d)
+   end
+end
+
+function nntest.SplitTable()
+   local input = torch.randn(3,4,5)
+   local module
+   for d = 1,input:dim() do
+      module = nn.SplitTable(d)
+      mytester:asserteq(#module:forward(input), input:size(d), "dimension " .. d)
+   end
+
+   -- Minibatch
+   local input = torch.randn(3,4,5)
+   local module
+   for d = 1,input:dim()-1 do
+      module = nn.SplitTable(d, 2)
+      mytester:asserteq(#module:forward(input), input:size(d+1), "dimension " .. d)
+   end
+end
+
 
 function nntest.View()
    local input = torch.rand(10)