Add minibatch support for nn.JoinTable and nn.SplitTable

The method setNumInputDims allows forwarding both minibatch and
non-minibatch tensors through the same module.
If this method is not used, the behaviour of these modules is the
same as before.

1 files changed, 39 insertions, 0 deletions

diff --git a/test/test.lua b/test/test.lua
index be17fd7..2db6f2d 100644
--- a/test/test.lua
+++ b/test/test.lua
@@ -1790,6 +1790,45 @@ function nntest.LookupTable()
    mytester:asserteq(berr, 0, torch.typename(module) .. ' - i/o backward err ')
 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)
+      module:setNumInputDims(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)
+      module:setNumInputDims(2)
+      mytester:asserteq(#module:forward(input), input:size(d+1), "dimension " .. d)
+   end
+end
+
+
 mytester:add(nntest)
 
 if not nn then