diff options
Diffstat (limited to 'test.lua')
| -rw-r--r-- | test.lua | 120 |
1 files changed, 92 insertions, 28 deletions
@@ -2009,38 +2009,44 @@ 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 + 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 module = nn.SpatialMaxPooling(ki,kj,si,sj) - 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: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() @@ -3012,6 +3018,13 @@ function nntest.SplitTable() module = nn.SplitTable(d, 2) mytester:asserteq(#module:forward(input), input:size(d+1), "dimension " .. d) end + + -- Negative indices + local module = nn.SplitTable(-3) + local input = torch.randn(3,4,5) + mytester:asserteq(#module:forward(input), 3, "negative index") + local input = torch.randn(2,3,4,5) + mytester:asserteq(#module:forward(input), 3, "negative index (minibatch)") end function nntest.SelectTable() @@ -3040,8 +3053,20 @@ function nntest.SelectTable() equal(gradInput[idx], gradOutputs[idx], "gradInput[idx] dimension " .. idx) equal(gradInput[nonIdx[idx]], zeros[nonIdx[idx]], "gradInput[nonIdx] dimension " .. idx) end - module:float() + + -- test negative index + local idx = -2 + module = nn.SelectTable(idx) + local output = module:forward(input) + equal(output, input[#input+idx+1], "output dimension " .. idx) + local gradInput = module:backward(input, gradOutputs[#input+idx+1]) + equal(gradInput[#input+idx+1], gradOutputs[#input+idx+1], "gradInput[idx] dimension " .. idx) + equal(gradInput[nonIdx[#input+idx+1]], zeros[nonIdx[#input+idx+1]], "gradInput[nonIdx] dimension " .. idx) + + -- test typecast local idx = #input + module = nn.SelectTable(idx) + module:float() local output = module:forward(input) equal(output, input[idx], "type output") local gradInput = module:backward(input, gradOutputs[idx]) @@ -3180,6 +3205,45 @@ function nntest.MixtureTable() end end +function nntest.NarrowTable() + local input = torch.randn(3,10,4) + local gradOutput = torch.randn(3,3,4) + local nt = nn.NarrowTable(5,3) + local seq = nn.Sequential() + seq:add(nn.SplitTable(1,2)) + seq:add(nt) + seq:add(nn.JoinTable(1,1)) + seq:add(nn.Reshape(3,3,4)) + local seq2 = nn.Narrow(2,5,3) + local output = seq:forward(input) + local gradInput = seq:backward(input, gradOutput) + local output2 = seq2:forward(input) + local gradInput2 = seq2:backward(input, gradOutput) + mytester:assertTensorEq(output, output2, 0.0000001, "NarrowTable output err") + mytester:assertTensorEq(gradInput, gradInput2, 0.00001, "NarrowTable gradInput err") + + -- now try it with a smaller input + local input = input:narrow(2, 1, 8) + local output = seq:forward(input) + local gradInput = seq:backward(input, gradOutput) + local output2 = seq2:forward(input) + local gradInput2 = seq2:backward(input, gradOutput) + mytester:assertTensorEq(output, output2, 0.0000001, "NarrowTable small output err") + mytester:assertTensorEq(gradInput, gradInput2, 0.00001, "NarrowTable small gradInput err") + + -- test type-cast + local input = input:float() + local gradOutput = gradOutput:float() + seq:float() + seq2:float() + local output = seq:forward(input) + local gradInput = seq:backward(input, gradOutput) + local output2 = seq2:forward(input) + local gradInput2 = seq2:backward(input, gradOutput) + mytester:assertTensorEq(output, output2, 0.0000001, "NarrowTable output float err") + mytester:assertTensorEq(gradInput, gradInput2, 0.00001, "NarrowTable gradInput float err") +end + function nntest.View() local input = torch.rand(10) local template = torch.rand(5,2) |