Added SpatialUpSamplingNearest module.

1 files changed, 64 insertions, 0 deletions

diff --git a/test/test.lua b/test/test.lua
index 5db941a..7a23c5e 100644
--- a/test/test.lua
+++ b/test/test.lua
@@ -1886,6 +1886,70 @@ function nntest.View()
       "Error in minibatch nElement")
 end
 
+-- Define a test for SpatialUpSamplingCuda
+function nntest.SpatialUpSamplingNearest()
+  local scale = torch.random(2,4)
+  for dim = 3,4 do
+    local m = nn.SpatialUpSamplingNearest(scale)
+
+    -- Create a randomly sized dimD vector
+    local shape = {}
+    for i = 1, dim do
+      table.insert(shape, torch.random(2, 2+dim-1))
+    end
+
+    -- Check that the gradient is correct by using finite elements 
+    local input = torch.Tensor(unpack(shape)):zero()
+
+    local err = jac.testJacobian(m, input)
+    mytester:assertlt(err, precision, ' error on state ')
+
+    local ferr, berr = jac.testIO(m, input)
+    mytester:asserteq(ferr, 0, torch.typename(m)..' - i/o forward err ')
+    mytester:asserteq(berr, 0, torch.typename(m)..' - i/o backward err ')
+
+    -- Also check that the forward prop is correct.
+    input = torch.rand(unpack(shape))
+    local output = m:forward(input)
+
+    local feat
+    local nfeats
+    if input:dim() == 3 then
+      nfeats = shape[1]
+      feat = {0}
+    else
+      feat = {0, 0}
+      nfeats = shape[1] * shape[2]
+    end
+    feat[#feat+1] = 0  -- ydim
+    feat[#feat+1] = 0  -- xdim
+    local xdim = input:dim()
+    local ydim = input:dim()-1
+    local err = 0
+    for f = 1, nfeats do
+      if input:dim() == 4 then
+        feat[1] = math.floor((f-1) / shape[1]) + 1
+        feat[2] = math.mod((f-1), shape[2]) + 1
+      else
+        feat[1] = f
+      end
+      for y = 1, input:size(ydim) * scale do
+        for x = 1, input:size(xdim) * scale do
+          feat[ydim] = y
+          feat[xdim] = x
+          local oval = output[feat]
+          feat[ydim] = math.floor((y-1)/scale)+1
+          feat[xdim] = math.floor((x-1)/scale)+1
+          local ival = input[feat] 
+          err = math.max(err, math.abs(oval-ival))
+        end
+      end
+    end
+
+    mytester:assertlt(err, precision, ' fprop is incorrect ')
+  end
+end
+
 mytester:add(nntest)
 
 if not nn then