Merge pull request #309 from fmassa/max_pool_pad

SpatialMaxPooling supports padding and ceil mode

1 files changed, 33 insertions, 27 deletions

diff --git a/test.lua b/test.lua
index 92b686f..ed774bd 100644
--- a/test.lua
+++ b/test.lua
@@ -1926,38 +1926,44 @@ function nntest.SpatialSubSampling()
 end
 
 function nntest.SpatialMaxPooling()
-   local from = math.random(1,5)
-   local ki = math.random(1,4)
-   local kj = math.random(1,4)
-   local si = math.random(1,3)
-   local sj = math.random(1,3)
-   local outi = math.random(4,5)
-   local outj = math.random(4,5)
-   local ini = (outi-1)*si+ki
-   local inj = (outj-1)*sj+kj
-
-   local module = nn.SpatialMaxPooling(ki,kj,si,sj)
-   local input = torch.rand(from,ini,inj)
+   for _,ceil_mode in pairs({true,false}) do
+      local from = math.random(1,5)
+      local ki = math.random(1,4)
+      local kj = math.random(1,4)
+      local si = math.random(1,3)
+      local sj = math.random(1,3)
+      local outi = math.random(4,5)
+      local outj = math.random(4,5)
+      local padW = math.min(math.random(0,1),math.floor(ki/2))
+      local padH =  math.min(math.random(0,1),math.floor(kj/2))
+      local ini = (outi-1)*si+ki-2*padW
+      local inj = (outj-1)*sj+kj-2*padH
+ 
+      local ceil_string = ceil_mode and 'ceil' or 'floor'
+      local module = nn.SpatialMaxPooling(ki,kj,si,sj,padW,padH)
+      if ceil_mode then module:ceil() else module:floor() end
+      local input = torch.rand(from,inj,ini)
 
-   local err = jac.testJacobian(module, input)
-   mytester:assertlt(err, precision, 'error on state ')
-
-   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 ')
+      local err = jac.testJacobian(module, input)
+      mytester:assertlt(err, precision, 'error '..ceil_string..' mode on state ')
 
-   -- batch
-   local nbatch = math.random(2,5)
-   input = torch.rand(nbatch,from,ini,inj)
-   module = nn.SpatialMaxPooling(ki,kj,si,sj)
+      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 ')
 
-   local err = jac.testJacobian(module, input)
-   mytester:assertlt(err, precision, 'error on state (Batch) ')
+      -- batch
+      local nbatch = math.random(2,5)
+      input = torch.rand(nbatch,from,inj,ini)
+      module = nn.SpatialMaxPooling(ki,kj,si,sj,padW,padH)
+      if ceil_mode then module:ceil() else module:floor() end
 
-   local ferr, berr = jac.testIO(module, input)
-   mytester:asserteq(ferr, 0, torch.typename(module) .. ' - i/o forward err (Batch) ')
-   mytester:asserteq(berr, 0, torch.typename(module) .. ' - i/o backward err (Batch) ')
+      local err = jac.testJacobian(module, input)
+      mytester:assertlt(err, precision, 'error '..ceil_string..' mode on state (Batch)')
 
+      local ferr, berr = jac.testIO(module, input)
+      mytester:asserteq(ferr, 0, torch.typename(module) .. ' - i/o forward err (Batch) ')
+      mytester:asserteq(berr, 0, torch.typename(module) .. ' - i/o backward err (Batch) ')
+  end
 end
 
 function nntest.SpatialAveragePooling()