Added nn.ModuleFromCriterion to wrap a called Criterion.

1 files changed, 57 insertions, 0 deletions

diff --git a/test/test_ModuleFromCriterion.lua b/test/test_ModuleFromCriterion.lua
new file mode 100644
index 0000000..78d3cd2
--- /dev/null
+++ b/test/test_ModuleFromCriterion.lua
@@ -0,0 +1,57 @@
+
+require 'totem'
+require 'nngraph'
+local test = {}
+local tester = totem.Tester()
+
+function test.test_call()
+    local prediction = nn.Identity()()
+    local target = nn.Identity()()
+    local mse = nn.MSECriterion()({prediction, target})
+    local costBits = nn.MulConstant(1/math.log(2))(mse)
+    local net = nn.gModule({prediction, target}, {costBits})
+
+    local input = {torch.randn(3, 5), torch.rand(3, 5)}
+    local criterion = nn.MSECriterion()
+    local output = net:forward(input)
+    criterion:forward(input[1], input[2])
+    tester:eq(output[1], criterion.output/math.log(2), "output", 1e-14)
+
+    local gradOutput = torch.randn(1)
+    local gradInput = net:backward(input, gradOutput)
+    criterion:backward(input[1], input[2])
+    tester:eq(gradInput[1], criterion.gradInput:clone():mul(gradOutput[1]/math.log(2)), "gradPrediction", 1e-14)
+    tester:eq(gradInput[2], torch.zeros(input[2]:size()), "gradTarget")
+end
+
+function test.test_grad()
+    local prediction = nn.Identity()()
+    local zero = nn.MulConstant(0)(prediction)
+    -- The target is created inside of the nngraph
+    -- to ignore the zero gradTarget.
+    local target = nn.AddConstant(1.23)(zero)
+    local mse = nn.MSECriterion()({prediction, target})
+    local net = nn.gModule({prediction}, {mse})
+
+    local input = torch.randn(4, 7)
+    totem.nn.checkGradients(tester, net, input)
+end
+
+local function module()
+    local module = nn.ModuleFromCriterion(nn.MSECriterion())
+    local input = {torch.randn(3, 5), torch.randn(3, 5)}
+    return module, input
+end
+
+function test.test_serializable()
+    local module, input = module()
+    totem.nn.checkSerializable(tester, module, input)
+end
+
+function test.test_typeCastable()
+    local module, input = module()
+    totem.nn.checkTypeCastable(tester, module, input)
+end
+
+
+tester:add(test):run()