Add cudaHalf, cudaDouble functions to nn.Module. (#395)

* Add cudaHalf, cudaDouble functions to nn.Module.

THCUNN.lua

@@ -155,4 +155,14 @@ THNN.kernels['torch.CudaHalfTensor'] = raw_half_functions
 torch.getmetatable('torch.CudaHalfTensor').THNN = THNN.kernels['torch.CudaHalfTensor']
 end
 
+local function Module__converter(type)
+    return function(self)
+            return self:type(type)
+    end
+end
+
+rawset(torch.getmetatable('nn.Module'), 'cudaDouble', Module__converter('torch.CudaDoubleTensor'))
+if cutorch.hasHalf then
+    rawset(torch.getmetatable('nn.Module'), 'cudaHalf', Module__converter('torch.CudaHalfTensor'))
+end
 return THCUNN

test.lua

@@ -5439,6 +5439,25 @@ function cunntest.VolumetricReplicationPadding_backward()
    end
 end
 
+function cunntest.ModuleConversionFunctions()
+   local module = nn.Tanh() -- arbitrary module
+   local input = torch.randn(10)
+
+   module:cuda()
+   mytester:assert(module:type() == 'torch.CudaTensor')
+   module:forward(input:type('torch.CudaTensor'))
+
+   module:cudaDouble()
+   mytester:assert(module:type() == 'torch.CudaDoubleTensor')
+   module:forward(input:type('torch.CudaDoubleTensor'))
+
+   if cutorch.hasHalf then
+      module:cudaHalf()
+      mytester:assert(module:type() == 'torch.CudaHalfTensor')
+      module:forward(input:type('torch.CudaHalfTensor'))
+   end
+end
+
 function cunntest.GPU()
    local ndevice = cutorch.getDeviceCount()
    if ndevice < 2 then