diff options
Diffstat (limited to 'test/test_nngraph.lua')
| -rw-r--r-- | test/test_nngraph.lua | 502 |
1 files changed, 232 insertions, 270 deletions
diff --git a/test/test_nngraph.lua b/test/test_nngraph.lua index ac47be2..8dac03a 100644 --- a/test/test_nngraph.lua +++ b/test/test_nngraph.lua @@ -5,358 +5,320 @@ local test = {} local tester = totem.Tester() local function checkGradients(...) - totem.nn.checkGradients(tester, ...) + totem.nn.checkGradients(tester, ...) end function test.test_oneOutput() - local in1 = nn.Identity()() - local out1 = nn.Identity()(in1) - local module = nn.gModule({in1}, {out1}) - - local input = torch.Tensor({1}) - module:forward(input) - tester:eq(module.output, torch.Tensor{1}, "output") - local gradInput = module:backward(input, torch.Tensor({-123})) - tester:eq(gradInput, torch.Tensor{-123}, "gradInput") - - local input2 = torch.Tensor({2}) - module:forward(input2) - tester:eq(module.output, torch.Tensor{2}, "output for input2") - gradInput = module:backward(input2, torch.Tensor({-2})) - tester:eq(gradInput, torch.Tensor{-2}, "expecting a recomputed gradInput") + local in1 = nn.Identity()() + local out1 = nn.Identity()(in1) + local module = nn.gModule({in1}, {out1}) + + local input = torch.Tensor({1}) + module:forward(input) + tester:eq(module.output, torch.Tensor{1}, "output") + local gradInput = module:backward(input, torch.Tensor({-123})) + tester:eq(gradInput, torch.Tensor{-123}, "gradInput") + + local input2 = torch.Tensor({2}) + module:forward(input2) + tester:eq(module.output, torch.Tensor{2}, "output for input2") + gradInput = module:backward(input2, torch.Tensor({-2})) + tester:eq(gradInput, torch.Tensor{-2}, "expecting a recomputed gradInput") end function test.test_twoOutputs() - local in1 = nn.Identity()() - local out1 = nn.Identity()(in1) - local out2 = nn.Identity()(in1) - local module = nn.gModule({in1}, {out1, out2}) - - local input = torch.Tensor({1}) - module:forward(input) - local gradInput = module:backward(input, {torch.Tensor({-2}), torch.Tensor({-3})}) - tester:eq(gradInput, torch.Tensor{-5}, "gradInput of a fork") - checkGradients(module, input) + local in1 = nn.Identity()() + local out1 = nn.Identity()(in1) + local out2 = nn.Identity()(in1) + local module = nn.gModule({in1}, {out1, out2}) + + local input = torch.Tensor({1}) + module:forward(input) + local gradInput = module:backward(input, {torch.Tensor({-2}), torch.Tensor({-3})}) + tester:eq(gradInput, torch.Tensor{-5}, "gradInput of a fork") + checkGradients(module, input) end function test.test_twoGradOutputs() - local in1 = nn.Sigmoid()() - local splitTable = nn.SplitTable(1)({in1}) - local out1, out2 = splitTable:split(2) - local module = nn.gModule({in1}, {out1, out2}) - - local input = torch.randn(2, 3) - local output = module:forward(input) - assert(#output == 2, "wrong number of outputs") - module:backward(input, {torch.randn(3), torch.randn(3)}) - checkGradients(module, input) + local in1 = nn.Sigmoid()() + local splitTable = nn.SplitTable(1)({in1}) + local out1, out2 = splitTable:split(2) + local module = nn.gModule({in1}, {out1, out2}) + + local input = torch.randn(2, 3) + local output = module:forward(input) + assert(#output == 2, "wrong number of outputs") + module:backward(input, {torch.randn(3), torch.randn(3)}) + checkGradients(module, input) end function test.test_twoInputs() - local in1 = nn.Identity()() - local in2 = nn.Identity()() - local prevH, prevCell = in2:split(2) - - local out1 = nn.CMulTable()({in1, prevH, prevCell}) - local module = nn.gModule({in1, in2}, {out1}) - - local input = {torch.randn(3), {torch.randn(3), torch.randn(3)}} - module:forward(input) - local gradInput = module:backward(input, torch.randn(3)) - assert(#gradInput == 2, "wrong number of gradInputs") - assert(type(gradInput[2]) == "table", "wrong gradInput[2] type") - checkGradients(module, input) + local in1 = nn.Identity()() + local in2 = nn.Identity()() + local prevH, prevCell = in2:split(2) + + local out1 = nn.CMulTable()({in1, prevH, prevCell}) + local module = nn.gModule({in1, in2}, {out1}) + + local input = {torch.randn(3), {torch.randn(3), torch.randn(3)}} + module:forward(input) + local gradInput = module:backward(input, torch.randn(3)) + assert(#gradInput == 2, "wrong number of gradInputs") + assert(type(gradInput[2]) == "table", "wrong gradInput[2] type") + checkGradients(module, input) end function test.test_twoInputs2() - local in1 = nn.Sigmoid()() - local in2 = nn.Sigmoid()() - local module = nn.gModule({in1, in2}, {in1, in2, nn.Sigmoid()(in1)}) - - local input = {torch.randn(3), torch.randn(3)} - module:forward(input) - local gradInput = module:backward(input, {torch.randn(3), torch.randn(3), torch.randn(3)}) - checkGradients(module, input) + local in1 = nn.Sigmoid()() + local in2 = nn.Sigmoid()() + local module = nn.gModule({in1, in2}, {in1, in2, nn.Sigmoid()(in1)}) + + local input = {torch.randn(3), torch.randn(3)} + module:forward(input) + local gradInput = module:backward(input, {torch.randn(3), torch.randn(3), torch.randn(3)}) + checkGradients(module, input) end function test.test_splitDebugLabels() - local node = nn.Identity()() - node.data.annotations._debugLabel = "node" - local node1, node2 = node:split(2) - assert(node1.data.annotations._debugLabel == "node-1") - assert(node2.data.annotations._debugLabel == "node-2") + local node = nn.Identity()() + node.data.annotations._debugLabel = "node" + local node1, node2 = node:split(2) + assert(node1.data.annotations._debugLabel == "node-1") + assert(node2.data.annotations._debugLabel == "node-2") end function test.test_identity() - local in1 = nn.Identity()() - local in2 = nn.Identity()() - local module = nn.gModule({in1, in2}, {in1, in2, nn.Identity()(in1)}) - - local input = {torch.randn(3), torch.randn(3)} - module:forward(input) - module:backward(input, {torch.randn(3), torch.randn(3), torch.randn(3)}) - checkGradients(module, input) + local in1 = nn.Identity()() + local in2 = nn.Identity()() + local module = nn.gModule({in1, in2}, {in1, in2, nn.Identity()(in1)}) + + local input = {torch.randn(3), torch.randn(3)} + module:forward(input) + module:backward(input, {torch.randn(3), torch.randn(3), torch.randn(3)}) + checkGradients(module, input) end function test.test_gradInputType() - local xInput = torch.randn(3) - local h = torch.randn(3) - - local x = nn.Identity()() - local prevRnnState = nn.Identity()() - local prevH1, prevCell = prevRnnState:split(2) - local prevH = prevH1 - - local cellOut = nn.CAddTable()({ - nn.CMulTable()({x, prevH}), - nn.CMulTable()({prevH, prevCell})}) - local module = nn.gModule({x, prevRnnState}, {cellOut}) - - local c = torch.randn(h:size()) - local prevRnnState = {h, c} - local input = {xInput, prevRnnState} - local output = module:forward(input) - - local gradOutput = torch.randn(h:size()) - local gradInput = module:backward(input, gradOutput) - - local gradX, gradPrevState = unpack(gradInput) - local gradPrevH, gradPrevCell = unpack(gradPrevState) - assert(type(gradPrevH) == type(h), "wrong gradPrevH type") - - tester:eq(type(gradPrevH), type(h), "wrong gradPrevH type") - tester:eq(gradPrevH:size(), h:size(), "wrong gradPrevH size") - checkGradients(module, input) + local xInput = torch.randn(3) + local h = torch.randn(3) + + local x = nn.Identity()() + local prevRnnState = nn.Identity()() + local prevH1, prevCell = prevRnnState:split(2) + local prevH = prevH1 + + local cellOut = nn.CAddTable()({ + nn.CMulTable()({x, prevH}), + nn.CMulTable()({prevH, prevCell})}) + local module = nn.gModule({x, prevRnnState}, {cellOut}) + + local c = torch.randn(h:size()) + local prevRnnState = {h, c} + local input = {xInput, prevRnnState} + local output = module:forward(input) + + local gradOutput = torch.randn(h:size()) + local gradInput = module:backward(input, gradOutput) + + local gradX, gradPrevState = unpack(gradInput) + local gradPrevH, gradPrevCell = unpack(gradPrevState) + assert(type(gradPrevH) == type(h), "wrong gradPrevH type") + + tester:eq(type(gradPrevH), type(h), "wrong gradPrevH type") + tester:eq(gradPrevH:size(), h:size(), "wrong gradPrevH size") + checkGradients(module, input) end function test.test_tabularInput() - local in1 = nn.SplitTable(1)() - local out1 = nn.CAddTable()(in1) - local module = nn.gModule({in1}, {out1}) + local in1 = nn.SplitTable(1)() + local out1 = nn.CAddTable()(in1) + local module = nn.gModule({in1}, {out1}) - local input = torch.randn(2, 3) - checkGradients(module, input) + local input = torch.randn(2, 3) + checkGradients(module, input) end function test.test_extraTable() - local in1 = nn.Identity()() - local out1 = nn.Identity()(in1) - local module = nn.gModule({in1}, {out1}) + local in1 = nn.Identity()() + local out1 = nn.Identity()(in1) + local module = nn.gModule({in1}, {out1}) - local input = torch.Tensor({123}) - tester:eq(module:forward(input), input, "simple output") - tester:eq(module:forward({input}), {input}, "tabular output") + local input = torch.Tensor({123}) + tester:eq(module:forward(input), input, "simple output") + tester:eq(module:forward({input}), {input}, "tabular output") end function test.test_accGradParameters() - local input = torch.randn(10) + local input = torch.randn(10) - local in1 = nn.CMul(input:nElement())() - local out1 = nn.Identity()(in1) - local out2 = nn.Identity()(in1) - local module = nn.gModule({in1}, {out1, out2}) - checkGradients(module, input) + local in1 = nn.CMul(input:nElement())() + local out1 = nn.Identity()(in1) + local out2 = nn.Identity()(in1) + local module = nn.gModule({in1}, {out1, out2}) + checkGradients(module, input) end function test.test_example1() - local x1 = nn.Linear(20,10)() - local mout = nn.Linear(10,1)(nn.Tanh()(nn.Linear(10,10)(nn.Tanh()(x1)))) - local mlp = nn.gModule({x1},{mout}) + local x1 = nn.Linear(20,10)() + local mout = nn.Linear(10,1)(nn.Tanh()(nn.Linear(10,10)(nn.Tanh()(x1)))) + local mlp = nn.gModule({x1},{mout}) - local x = torch.rand(20) - checkGradients(mlp, x) + local x = torch.rand(20) + checkGradients(mlp, x) end function test.test_example2() - local x1=nn.Linear(20,20)() - local x2=nn.Linear(10,10)() - local m0=nn.Linear(20,1)(nn.Tanh()(x1)) - local m1=nn.Linear(10,1)(nn.Tanh()(x2)) - local madd=nn.CAddTable()({m0,m1}) - local m2=nn.Sigmoid()(madd) - local m3=nn.Tanh()(madd) - local gmod = nn.gModule({x1,x2},{m2,m3}) - - local x = torch.rand(20) - local y = torch.rand(10) - checkGradients(gmod, {x, y}) + local x1=nn.Linear(20,20)() + local x2=nn.Linear(10,10)() + local m0=nn.Linear(20,1)(nn.Tanh()(x1)) + local m1=nn.Linear(10,1)(nn.Tanh()(x2)) + local madd=nn.CAddTable()({m0,m1}) + local m2=nn.Sigmoid()(madd) + local m3=nn.Tanh()(madd) + local gmod = nn.gModule({x1,x2},{m2,m3}) + + local x = torch.rand(20) + local y = torch.rand(10) + checkGradients(gmod, {x, y}) end function test.test_example3() - local m = nn.Sequential() - m:add(nn.SplitTable(1)) - m:add(nn.ParallelTable():add(nn.Linear(10,20)):add(nn.Linear(10,30))) - local input = nn.Identity()() - local input1,input2 = m(input):split(2) - local m3 = nn.JoinTable(1)({input1,input2}) - local g = nn.gModule({input},{m3}) - - local indata = torch.rand(2,10) - checkGradients(g, indata) + local m = nn.Sequential() + m:add(nn.SplitTable(1)) + m:add(nn.ParallelTable():add(nn.Linear(10,20)):add(nn.Linear(10,30))) + local input = nn.Identity()() + local input1,input2 = m(input):split(2) + local m3 = nn.JoinTable(1)({input1,input2}) + local g = nn.gModule({input},{m3}) + + local indata = torch.rand(2,10) + checkGradients(g, indata) end function test.test_example4() - local input = nn.Identity()() - local L1 = nn.Tanh()(nn.Linear(1,2)(input)) - local L2 = nn.Tanh()(nn.Linear(3,6)(nn.JoinTable(1)({input,L1}))) - local L3 = nn.Tanh()(nn.Linear(8,16)(nn.JoinTable(1)({L1,L2}))) - local g = nn.gModule({input},{L3}) - - local indata = torch.rand(1) - checkGradients(g, indata) + local input = nn.Identity()() + local L1 = nn.Tanh()(nn.Linear(1,2)(input)) + local L2 = nn.Tanh()(nn.Linear(3,6)(nn.JoinTable(1)({input,L1}))) + local L3 = nn.Tanh()(nn.Linear(8,16)(nn.JoinTable(1)({L1,L2}))) + local g = nn.gModule({input},{L3}) + + local indata = torch.rand(1) + checkGradients(g, indata) end function test.test_type() - local in1 = nn.Linear(20,10)() - local out1 = nn.Linear(10,1)(nn.Tanh()(nn.Linear(10,10)(nn.Tanh()(in1)))) - local module = nn.gModule({in1}, {out1}) - local input = torch.rand(20) - local output = module:forward(input) - module:backward(input, output) - tester:eq(torch.typename(output), "torch.DoubleTensor") - tester:eq(torch.typename(module.output), "torch.DoubleTensor") - tester:eq(torch.typename(module.gradInput), "torch.DoubleTensor") - tester:eq(torch.typename(module.innode.data.input[1]), "torch.DoubleTensor") - tester:eq(torch.typename(module.outnode.data.input[1]), "torch.DoubleTensor") - tester:eq(torch.typename(module.forwardnodes[1].data.input[1]), "torch.DoubleTensor") - - module:float() - local output = module:forward(input:float()) - tester:eq(torch.typename(output), "torch.FloatTensor") - tester:eq(torch.typename(module.output), "torch.FloatTensor") - tester:eq(torch.typename(module.gradInput), "torch.FloatTensor") - tester:eq(torch.typename(module.innode.data.input[1]), "torch.FloatTensor") - tester:eq(torch.typename(module.outnode.data.input[1]), "torch.FloatTensor") - tester:eq(torch.typename(module.forwardnodes[1].data.input[1]), "torch.FloatTensor") + local in1 = nn.Linear(20,10)() + local out1 = nn.Linear(10,1)(nn.Tanh()(nn.Linear(10,10)(nn.Tanh()(in1)))) + local module = nn.gModule({in1}, {out1}) + local input = torch.rand(20) + local output = module:forward(input) + module:backward(input, output) + tester:eq(torch.typename(output), "torch.DoubleTensor") + tester:eq(torch.typename(module.output), "torch.DoubleTensor") + tester:eq(torch.typename(module.gradInput), "torch.DoubleTensor") + tester:eq(torch.typename(module.innode.data.input[1]), "torch.DoubleTensor") + tester:eq(torch.typename(module.outnode.data.input[1]), "torch.DoubleTensor") + tester:eq(torch.typename(module.forwardnodes[1].data.input[1]), "torch.DoubleTensor") + + module:float() + local output = module:forward(input:float()) + tester:eq(torch.typename(output), "torch.FloatTensor") + tester:eq(torch.typename(module.output), "torch.FloatTensor") + tester:eq(torch.typename(module.gradInput), "torch.FloatTensor") + tester:eq(torch.typename(module.innode.data.input[1]), "torch.FloatTensor") + tester:eq(torch.typename(module.outnode.data.input[1]), "torch.FloatTensor") + tester:eq(torch.typename(module.forwardnodes[1].data.input[1]), "torch.FloatTensor") end function test.test_nestedGradInput() - local x = nn.Identity()() - local h1 = nn.Sequential():add(nn.JoinTable(2)):add(nn.Tanh()) - local h2 = nn.Sequential():add(nn.JoinTable(2)):add(nn.Identity()) - local out = nn.CAddTable()({h1(x), h2(x)}) + local x = nn.Identity()() + local h1 = nn.Sequential():add(nn.JoinTable(2)):add(nn.Tanh()) + local h2 = nn.Sequential():add(nn.JoinTable(2)):add(nn.Identity()) + local out = nn.CAddTable()({h1(x), h2(x)}) - local model = nn.gModule({x}, {out}) + local model = nn.gModule({x}, {out}) - local input = {} - input[1] = torch.randn(3, 3) - input[2] = torch.randn(3, 3) - input[3] = torch.randn(3, 3) + local input = {} + input[1] = torch.randn(3, 3) + input[2] = torch.randn(3, 3) + input[3] = torch.randn(3, 3) - checkGradients(model, input) + checkGradients(model, input) - local input = {} - input[1] = torch.randn(2, 3) - input[2] = torch.randn(2, 3) - input[3] = torch.randn(2, 3) + local input = {} + input[1] = torch.randn(2, 3) + input[2] = torch.randn(2, 3) + input[3] = torch.randn(2, 3) - checkGradients(model, input) + checkGradients(model, input) end function test.test_unusedInput() - local x = nn.Identity()() - local h = nn.Identity()() - local h2 = nn.Identity()() + local x = nn.Identity()() + local h = nn.Identity()() + local h2 = nn.Identity()() - local ok, result = pcall(nn.gModule, {x, h}, {x}) - assert(not ok, "the unused input should be detected") + local ok, result = pcall(nn.gModule, {x, h}, {x}) + assert(not ok, "the unused input should be detected") end function test.test_unusedChild() - local prevState = nn.Identity()() - local h, cell = prevState:split(2) + local prevState = nn.Identity()() + local h, cell = prevState:split(2) - local ok, result = pcall(nn.gModule, {prevState}, {h}) - assert(not ok, "the unused cell should be detected") + local ok, result = pcall(nn.gModule, {prevState}, {h}) + assert(not ok, "the unused cell should be detected") end function test.test_nilInput() - local ok, result = pcall(function() nn.Sigmoid()(nil) end) - assert(not ok, "the nil input should be detected") + local ok, result = pcall(function() nn.Sigmoid()(nil) end) + assert(not ok, "the nil input should be detected") end function test.test_unusedNode() - local in1 = nn.Identity()() - local in2 = nn.Identity()() - local middleResult = nn.Sigmoid()(in2) - local out1 = nn.Sigmoid()(in1) + local in1 = nn.Identity()() + local in2 = nn.Identity()() + local middleResult = nn.Sigmoid()(in2) + local out1 = nn.Sigmoid()(in1) - local ok, result = pcall(nn.gModule, {in1, in2}, {out1}) - assert(not ok, "the unused middleResult should be detected") + local ok, result = pcall(nn.gModule, {in1, in2}, {out1}) + assert(not ok, "the unused middleResult should be detected") end function test.test_usageAfterSplit() - local prevState = nn.Identity()() - local h, cell = prevState:split(2) - local nextState = nn.Identity()(prevState) - local transformed = nn.Sigmoid()(cell) - - local model = nn.gModule({prevState}, {h, nextState, transformed}) - local nHidden = 10 - local input = {torch.randn(nHidden), torch.randn(nHidden)} - checkGradients(model, input) + local prevState = nn.Identity()() + local h, cell = prevState:split(2) + local nextState = nn.Identity()(prevState) + local transformed = nn.Sigmoid()(cell) + + local model = nn.gModule({prevState}, {h, nextState, transformed}) + local nHidden = 10 + local input = {torch.randn(nHidden), torch.randn(nHidden)} + checkGradients(model, input) end function test.test_resizeNestedAs() - local in1 = nn.Identity()() - local out1 = nn.Identity()(in1) - local out2 = nn.Identity()(in1) - - local net = nn.gModule({in1}, {out1, out2}) - local input = {torch.randn(10), {torch.randn(3), torch.randn(4)}} - net:forward(input) - net:backward(input, net.output) - checkGradients(net, input) - - input = {torch.randn(10), {torch.randn(3), torch.randn(4), torch.randn(5)}} - net:forward(input) - net:backward(input, net.output) - checkGradients(net, input) - - input = {torch.randn(10), {torch.randn(3), torch.randn(4)}} - net:forward(input) - local gradInput = net:backward(input, net.output) - tester:eq(#(gradInput[2]), 2, "gradInput[2] size") - checkGradients(net, input) + local in1 = nn.Identity()() + local out1 = nn.Identity()(in1) + local out2 = nn.Identity()(in1) + + local net = nn.gModule({in1}, {out1, out2}) + local input = {torch.randn(10), {torch.randn(3), torch.randn(4)}} + net:forward(input) + net:backward(input, net.output) + checkGradients(net, input) + + input = {torch.randn(10), {torch.randn(3), torch.randn(4), torch.randn(5)}} + net:forward(input) + net:backward(input, net.output) + checkGradients(net, input) + + input = {torch.randn(10), {torch.randn(3), torch.randn(4)}} + net:forward(input) + local gradInput = net:backward(input, net.output) + tester:eq(#(gradInput[2]), 2, "gradInput[2] size") + checkGradients(net, input) end - -function test.test_annotateGraph() - local input = nn.Identity()():annotate( - {name = 'Input', description = 'DescA', - graphAttributes = {color = 'red'}}) - - local hidden_a = nn.Linear(10, 10)(input):annotate( - {name = 'Hidden A', description = 'DescB', - graphAttributes = {color = 'blue', fontcolor='green', tooltip = 'I am green'}}) - local hidden_b = nn.Sigmoid()(hidden_a) - local output = nn.Linear(10, 10)(hidden_b) - local net = nn.gModule({input}, {output}) - - tester:assert(hidden_a:label():match('DescB')) - local fg_tmpfile = os.tmpname() - local bg_tmpfile = os.tmpname() - graph.dot(net.fg, 'Test', fg_tmpfile) - graph.dot(net.fg, 'Test BG', bg_tmpfile) - - local function checkDotFile(tmpfile) - local dotcontent = io.open(tmpfile .. '.dot', 'r'):read("*all") - tester:assert( - dotcontent:match('%[label=%"Input.*DescA.*%" color=red%]')) - tester:assert( - dotcontent:match( - '%[label=%"Hidden A.*DescB.*%".*fontcolor=green.*%]')) - tester:assert( - dotcontent:match('%[label=%".*DescB.*%".*color=blue.*%]')) - tester:assert( - dotcontent:match( - '%[label=%".*DescB.*%".*tooltip=%".*test_nngraph.lua.*%".*%]')) - end - - checkDotFile(fg_tmpfile) - checkDotFile(bg_tmpfile) -end - - tester:add(test):run() |