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-rw-r--r--test/test-all.lua62
1 files changed, 31 insertions, 31 deletions
diff --git a/test/test-all.lua b/test/test-all.lua
index 80ed910..b284cd4 100644
--- a/test/test-all.lua
+++ b/test/test-all.lua
@@ -3,7 +3,7 @@ local nnxtest = {}
local precision = 1e-5
local mytester
--- you can easily test specific units like this:
+-- you can easily test specific units like this:
-- th -lnnx -e "nnx.test{'MultiSoftMax'}"
-- th -lnnx -e "nnx.test{'SoftMaxTree', 'Balance'}"
@@ -16,7 +16,7 @@ function nnxtest.SpatialPadding()
local pad_t = math.random(0,8)
local pad_b = math.random(0,8)
local val = torch.randn(1):squeeze()
- local module = nn.SpatialPadding(pad_l, pad_r, pad_t, pad_b, val)
+ local module = nn.SpatialPadding(pad_l, pad_r, pad_t, pad_b, nil, nil, val)
local input = torch.rand(fanin,sizey,sizex)
local err = nn.Jacobian.testJacobian(module, input)
@@ -82,10 +82,10 @@ end
local function template_SpatialReSamplingEx(up, mode)
for iTest = 1,3 do
- local nDims = math.random(2,6)
+ local nDims = math.random(2,3)
local dims = torch.LongStorage(nDims)
for i = 1,nDims do
- dims[i] = math.random(5,20/nDims)
+ dims[i] = math.random(5,torch.round(20/nDims))
end
local xratio, yratio
if up then
@@ -102,10 +102,10 @@ local function template_SpatialReSamplingEx(up, mode)
local module = nn.SpatialReSamplingEx({owidth=owidth_, oheight=oheight_,
xDim=xdim, yDim = ydim, mode=mode})
local input = torch.rand(dims)
-
+
local err = nn.Jacobian.testJacobian(module, input)
mytester:assertlt(err, precision, 'error on state ')
-
+
local ferr, berr = nn.Jacobian.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 ')
@@ -137,8 +137,8 @@ end
function nnxtest.SpatialDownSampling()
local fanin = math.random(1,4)
- local sizex = math.random(11,4)
- local sizey = math.random(11,4)
+ local sizex = math.random(4,11)
+ local sizey = math.random(4,11)
local mx = math.random(2,6)
local my = math.random(2,6)
local module = nn.SpatialDownSampling(mx,my)
@@ -172,21 +172,21 @@ function nnxtest.SpatialReSampling_1()
local batchSize = math.random(4,8)
local input2 = torch.rand(batchSize,fanin,sizey,sizex)
input2[2]:copy(input)
-
+
local output = module:forward(input):clone()
local output2 = module:forward(input2)
mytester:assertTensorEq(output, output2[2], 0.00001, 'SpatialResampling batch forward err')
-
+
local gradInput = module:backward(input, output):clone()
local gradInput2 = module:backward(input2, output2)
mytester:assertTensorEq(gradInput, gradInput2[2], 0.00001, 'SpatialResampling batch backward err')
-
+
-- test rwidth/rheight
local input = torch.randn(3,8,10)
local module = nn.SpatialReSampling{rwidth=0.5,rheight=0.5}
local output = module:forward(input)
mytester:assertTableEq(output:size():totable(), {3, 4, 5}, 0.00000001, 'SpatialResampling batch rwidth/rheight err')
-
+
local input = torch.randn(2,3,8,10)
local module = nn.SpatialReSampling{rwidth=0.5,rheight=0.5}
local output = module:forward(input)
@@ -408,7 +408,7 @@ local function template_SpatialMatching(channels, iwidth, iheight, maxw, maxh, f
local input = torch.rand(2, channels, iheight, iwidth)
local err = nn.Jacobian.testJacobian(module, input)
mytester:assertlt(err, precision, 'error on state ')
-
+
local ferr, berr = nn.Jacobian.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 ')
@@ -426,7 +426,7 @@ function nnxtest.SoftMaxTree()
local grad = torch.randn(5)
local root_id = 29
local hierarchy={
- [29]=torch.IntTensor{30,1,2}, [1]=torch.IntTensor{3,4,5},
+ [29]=torch.IntTensor{30,1,2}, [1]=torch.IntTensor{3,4,5},
[2]=torch.IntTensor{6,7,8}, [3]=torch.IntTensor{9,10,11},
[4]=torch.IntTensor{12,13,14}, [5]=torch.IntTensor{15,16,17},
[6]=torch.IntTensor{18,19,20}, [7]=torch.IntTensor{21,22,23},
@@ -439,7 +439,7 @@ function nnxtest.SoftMaxTree()
local indices = {3,3,4}
local parentIds = {29,2,8}
local linears = {}
-
+
for i,parentId in ipairs(parentIds) do
local s = nn.Sequential()
local linear = nn.Linear(100,hierarchy[parentId]:size(1))
@@ -512,7 +512,7 @@ end
function nnxtest.TreeNLLCriterion()
local input = torch.randn(5,10)
local target = torch.ones(5) --all targets are 1
- local c = nn.TreeNLLCriterion()
+ local c = nn.TreeNLLCriterion()
-- the targets are actually ignored (SoftMaxTree uses them before TreeNLLCriterion)
local err = c:forward(input, target)
gradInput = c:backward(input, target)
@@ -577,10 +577,10 @@ local function blur(mean, stdv, size)
end
function nnxtest.Balance()
- local inputSize = 7
+ local inputSize = 7
local batchSize = 3
local nBatch = 1
-
+
local input = torch.randn(batchSize, inputSize):mul(0.1):float()
for i=1,batchSize do
input[i]:add(blur(3, 1, inputSize):float())
@@ -591,36 +591,36 @@ function nnxtest.Balance()
local gradOutput = torch.randn(batchSize, inputSize):float()
local bl = nn.Balance(nBatch)
bl:float()
-
+
local output = bl:forward(input)
local p_y = output:sum(1):div(output:sum())
mytester:assert(p_y:std() < 0.02)
mytester:assert(math.abs(p_y:sum() - 1) < 0.000001)
-
+
local gradInput = bl:backward(input, gradOutput)
end
function nnxtest.MultiSoftMax()
- local inputSize = 7
+ local inputSize = 7
local nSoftmax = 5
local batchSize = 3
-
+
local input = torch.randn(batchSize, nSoftmax, inputSize)
local gradOutput = torch.randn(batchSize, nSoftmax, inputSize)
local msm = nn.MultiSoftMax()
-
+
local output = msm:forward(input)
local gradInput = msm:backward(input, gradOutput)
mytester:assert(output:isSameSizeAs(input))
mytester:assert(gradOutput:isSameSizeAs(gradInput))
-
+
local sm = nn.SoftMax()
local input2 = input:view(batchSize*nSoftmax, inputSize)
local output2 = sm:forward(input2)
local gradInput2 = sm:backward(input2, gradOutput:view(batchSize*nSoftmax, inputSize))
-
- mytester:assertTensorEq(output, output2, 0.000001)
- mytester:assertTensorEq(gradInput, gradInput2, 0.000001)
+
+ mytester:assertTensorEq(output:view(-1), output2:view(-1), 0.000001)
+ mytester:assertTensorEq(gradInput:view(-1), gradInput2:view(-1), 0.000001)
end
function nnxtest.PushPullTable()
@@ -630,14 +630,14 @@ function nnxtest.PushPullTable()
local gradOutput = torch.randn(5)
local root_id = 29
local hierarchy={
- [29]=torch.IntTensor{30,1,2}, [1]=torch.IntTensor{3,4,5},
+ [29]=torch.IntTensor{30,1,2}, [1]=torch.IntTensor{3,4,5},
[2]=torch.IntTensor{6,7,8}, [3]=torch.IntTensor{9,10,11},
[4]=torch.IntTensor{12,13,14}, [5]=torch.IntTensor{15,16,17},
[6]=torch.IntTensor{18,19,20}, [7]=torch.IntTensor{21,22,23},
[8]=torch.IntTensor{24,25,26,27,28}
}
local smt = nn.SoftMaxTree(100, hierarchy, root_id)
- -- create a network where inputs are fed through softmaxtree
+ -- create a network where inputs are fed through softmaxtree
-- and targets are teleported (pushed then pulled) to softmaxtree
local mlp = nn.Sequential()
local linear = nn.Linear(50,100)
@@ -663,7 +663,7 @@ function nnxtest.PushPullTable()
mytester:assertTensorEq(output, output2, 0.00001, "push/pull forward error")
mytester:assertTensorEq(gradInput[1], gradInput[1], 0.00001, "push/pull backward error")
mytester:assertTensorEq(gradInput[2], gradInput[2], 0.00001, "push/pull backward error")
-
+
-- test multi-pull case
local mlp = nn.Sequential()
local push = nn.PushTable(2)
@@ -680,7 +680,7 @@ function nnxtest.PushPullTable()
mytester:assertTensorEq(output[4], inputTable[2], 0.00001, "push/pull multi-forward error")
local gradOutput = {inputTable[2]:clone(), inputTable[1]:clone(), inputTable[2]:clone(), inputTable[2]:clone()}
local gradInput = mlp:backward(inputTable, gradOutput)
- local gradInput2 = inputTable[2]:clone():mul(3)
+ local gradInput2 = inputTable[2]:clone():mul(3)
mytester:assertTensorEq(gradInput[1], gradInput[1], 0.00001, "push/pull multi-backward error")
mytester:assertTensorEq(gradInput[2], gradInput[2], 0.00001, "push/pull multi-backward error")
end
generated by cgit v1.2.3 (git 2.25.1) at 2024-05-30 18:06:13 +0300