Add SpatialAdaptiveAveragePooling.

1 files changed, 56 insertions, 0 deletions

diff --git a/test.lua b/test.lua
index b3e1d16..8425aef 100644
--- a/test.lua
+++ b/test.lua
@@ -4009,6 +4009,62 @@ function nntest.SpatialAdaptiveMaxPooling()
 
 end
 
+function nntest.SpatialAdaptiveAveragePooling()
+   local from = math.random(1,5)
+   local ki = math.random(1,5)
+   local kj = math.random(1,5)
+   local ini = math.random(1,16)
+   local inj = math.random(1,16)
+
+   local module = nn.SpatialAdaptiveAveragePooling(ki,kj)
+   local input = torch.rand(from,ini,inj)
+
+   local err = jac.testJacobian(module, input)
+   mytester:assertlt(err, precision, 'error on state ')
+
+   local ferr, berr = jac.testIO(module, input)
+   mytester:eq(ferr, 0, torch.typename(module) .. ' - i/o forward err ', precision)
+   mytester:eq(berr, 0, torch.typename(module) .. ' - i/o backward err ', precision)
+
+   -- batch
+   local nbatch = math.random(1,3)
+   input = torch.rand(nbatch,from,ini,inj)
+   module = nn.SpatialAdaptiveAveragePooling(ki,kj)
+
+   local err = jac.testJacobian(module, input)
+   mytester:assertlt(err, precision, 'error on state (Batch) ')
+
+   local ferr, berr = jac.testIO(module, input)
+   mytester:eq(ferr, 0, torch.typename(module) .. ' - i/o forward err (Batch) ', precision)
+   mytester:eq(berr, 0, torch.typename(module) .. ' - i/o backward err (Batch) ', precision)
+
+   -- non-contiguous
+
+   input = torch.rand(from,ini,inj):transpose(2,3)
+   module = nn.SpatialAdaptiveAveragePooling(ki,kj)
+   local inputc = input:contiguous() -- contiguous
+   local output = module:forward(input):clone()
+   local outputc = module:forward(inputc):clone()
+   mytester:asserteq(0, (output-outputc):abs():max(), torch.typename(module) .. ' - non-contiguous err ')
+   local gradInput = module:backward(input, output):clone()
+   local gradInputc = module:backward(inputc, outputc):clone()
+   mytester:asserteq(0, (gradInput-gradInputc):abs():max(), torch.typename(module) .. ' - non-contiguous err ')
+
+   -- non-contiguous batch
+   local nbatch = math.random(1,3)
+   input = torch.rand(nbatch,from,ini,inj):transpose(1,3):transpose(2,4)
+   local inputc = input:contiguous() -- contiguous
+   module = nn.SpatialAdaptiveAveragePooling(ki,kj)
+
+   local output = module:forward(input):clone()
+   local outputc = module:forward(inputc):clone()
+   mytester:asserteq(0, (output-outputc):abs():max(), torch.typename(module) .. ' - batch non-contiguous err ')
+   local gradInput = module:backward(input, output):clone()
+   local gradInputc = module:backward(inputc, outputc):clone()
+   mytester:asserteq(0, (gradInput-gradInputc):abs():max(), torch.typename(module) .. ' - batch non-contiguous err ')
+
+end
+
 function nntest.SpatialLPPooling()
    local fanin = math.random(1,4)
    local osizex = math.random(1,4)