Relax equality tests from exact precision 0.

Expecting exact precision 0 can lead to failing tests if underlying implementation changes, a high expected precision of say 1e-15 seem more appropriate (nntest.Power sometimes fails because of this)

1 files changed, 24 insertions, 24 deletions

diff --git a/test/test.lua b/test/test.lua
index aebc755..022ee8a 100644
--- a/test/test.lua
+++ b/test/test.lua
@@ -218,7 +218,7 @@ function nntest.Power()
    local module = nn.Power(2)
    local out = module:forward(in1)
    local err = out:dist(in1:cmul(in1))
-   mytester:asserteq(err, 0, torch.typename(module) .. ' - forward err ')
+   mytester:assertlt(err, 1e-15, torch.typename(module) .. ' - forward err ')
 
    local ini = math.random(3,5)
    local inj = math.random(3,5)
@@ -241,7 +241,7 @@ function nntest.Square()
    local module = nn.Square()
    local out = module:forward(in1)
    local err = out:dist(in1:cmul(in1))
-   mytester:asserteq(err, 0, torch.typename(module) .. ' - forward err ')
+   mytester:assertlt(err, 1e-15, torch.typename(module) .. ' - forward err ')
 
    local ini = math.random(3,5)
    local inj = math.random(3,5)
@@ -263,7 +263,7 @@ function nntest.Sqrt()
    local module = nn.Sqrt()
    local out = module:forward(in1)
    local err = out:dist(in1:sqrt())
-   mytester:asserteq(err, 0, torch.typename(module) .. ' - forward err ')
+   mytester:assertlt(err, 1e-15, torch.typename(module) .. ' - forward err ')
 
    local ini = math.random(3,5)
    local inj = math.random(3,5)
@@ -1060,16 +1060,16 @@ function nntest.SpatialFullConvolution()
 end
 
 function nntest.SpatialFullConvolutionMap()
-   local from = math.ceil(torch.uniform(2,4))
-   local to = math.ceil(torch.uniform(2,5))
-   local fanin = math.ceil(torch.uniform(1, from))
+   local from = math.random(2,4)
+   local to = math.random(2,5)
+   local fanin = math.random(1, from)
    local tt = nn.tables.random(from, to, fanin)
-   local ki = math.ceil(torch.uniform(2,5))
-   local kj = math.ceil(torch.uniform(2,5))
-   local si = math.ceil(torch.uniform(1,3))
-   local sj = math.ceil(torch.uniform(1,3))
-   local ini = math.ceil(torch.uniform(5,7))
-   local inj = math.ceil(torch.uniform(5,7))
+   local ki = math.random(2,5)
+   local kj = math.random(2,5)
+   local si = math.random(1,3)
+   local sj = math.random(1,3)
+   local ini = math.random(5,7)
+   local inj = math.random(5,7)
    local module = nn.SpatialFullConvolutionMap(tt, ki, kj, si, sj)
    local input = torch.Tensor(from, inj, ini):zero()
    
@@ -1105,15 +1105,15 @@ function nntest.SpatialFullConvolutionMap()
 end
 
 function nntest.SpatialFullConvolutionCompare()
-    local from = math.ceil(torch.uniform(2,4))
-    local to = math.ceil(torch.uniform(2,5))
+    local from = math.random(2,4)
+    local to = math.random(2,5)
     local tt = nn.tables.full(from, to)
-    local ki = math.ceil(torch.uniform(2,5))
-    local kj = math.ceil(torch.uniform(2,5))
-    local si = math.ceil(torch.uniform(1,3))
-    local sj = math.ceil(torch.uniform(1,3))
-    local ini = math.ceil(torch.uniform(7,8))
-    local inj = math.ceil(torch.uniform(7,8))
+    local ki = math.random(2,5)
+    local kj = math.random(2,5)
+    local si = math.random(1,3)
+    local sj = math.random(1,3)
+    local ini = math.random(7,8)
+    local inj = math.random(7,8)
     local module1 = nn.SpatialFullConvolutionMap(tt, ki, kj, si, sj)
     local module2 = nn.SpatialFullConvolution(from, to, ki, kj, si, sj)
     local input = torch.rand(from, inj, ini)
@@ -1307,12 +1307,12 @@ end
 
 function nntest.SpatialMaxPooling()
    local from = math.random(1,5)
-   local ki = math.random(1,5)
-   local kj = 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(2,4)
-   local outj = math.random(2,4)
+   local outi = math.random(4,5)
+   local outj = math.random(4,5)
    local ini = (outi-1)*si+ki
    local inj = (outj-1)*sj+kj