TemporalMaxPooling batch unit tests

1 files changed, 24 insertions, 0 deletions

diff --git a/test/test.lua b/test/test.lua
index 29f76da..a0d7566 100644
--- a/test/test.lua
+++ b/test/test.lua
@@ -1413,12 +1413,36 @@ function nntest.TemporalMaxPooling()
    local module = nn.TemporalMaxPooling(ki, si)
    local input = torch.Tensor(ini, from):zero()
 
+   -- 1D
+   local err = jac.testJacobian(module, input)
+   mytester:assertlt(err, precision, 'error on state ')
+
+   local ferr, berr = jac.testIO(module, input)
+   mytester:asserteq(0, ferr, torch.typename(module) .. ' - i/o forward err ')
+   mytester:asserteq(0, berr, torch.typename(module) .. ' - i/o backward err ')
+
+   -- 2D
+   local nBatchFrame = 2
+   local input = torch.Tensor(nBatchFrame, ini, from):zero()
    local err = jac.testJacobian(module, input)
    mytester:assertlt(err, precision, 'error on state ')
 
    local ferr, berr = jac.testIO(module, input)
    mytester:asserteq(0, ferr, torch.typename(module) .. ' - i/o forward err ')
    mytester:asserteq(0, berr, torch.typename(module) .. ' - i/o backward err ')
+   
+   -- 2D matches 1D
+   local output = module:forward(input):clone()
+   local outputGrad = torch.randn(output:size())
+   local inputGrad = module:backward(input, outputGrad):clone()
+   
+   local input1D = input:select(1, 1)
+   local output1D = module:forward(input1D)
+   local outputGrad1D = outputGrad:select(1, 1)
+   local inputGrad1D = module:backward(input1D, outputGrad1D)
+   
+   mytester:assertTensorEq(output:select(1,1), output1D, 0.000001, 'error on 2D vs 1D forward)')
+   mytester:assertTensorEq(inputGrad:select(1,1), inputGrad1D, 0.000001, 'error on 2D vs 1D backward)')
 end
 
 function nntest.VolumetricConvolution()