/
nn_test.go
740 lines (625 loc) · 164 KB
/
nn_test.go
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package gorgonia
import (
"fmt"
"math/rand"
"testing"
"github.com/stretchr/testify/assert"
"github.com/stretchr/testify/require"
"gorgonia.org/dawson"
"gorgonia.org/tensor"
)
var im2colTests = []struct {
kernel tensor.Shape
pad tensor.Shape
stride tensor.Shape
dilation tensor.Shape
}{
{tensor.Shape{4, 4}, tensor.Shape{0, 0}, tensor.Shape{1, 1}, tensor.Shape{1, 1}},
{tensor.Shape{3, 3}, tensor.Shape{1, 1}, tensor.Shape{2, 2}, tensor.Shape{1, 1}},
{tensor.Shape{3, 3}, tensor.Shape{1, 1}, tensor.Shape{3, 3}, tensor.Shape{1, 1}},
}
func im2colTest(t *testing.T, dt tensor.Dtype, kernel, pad, stride, dilation tensor.Shape) {
assert := assert.New(t)
g := NewGraph()
x := NewTensor(g, dt, 4, WithShape(2, 1, 28, 28), WithInit(RangedFrom(0))) // mnist, in batches of 10
y, err := Im2Col(x, kernel, pad, stride, dilation)
if err != nil {
t.Error(err)
return
}
cost := Must(Sum(y))
grads, err := Grad(cost, x)
if err != nil {
t.Errorf("error while Grad(): %v", err)
return
}
m := NewTapeMachine(g, BindDualValues())
defer m.Close()
if err := m.RunAll(); err != nil {
t.Error(err)
return
}
// t.Logf("x: %v", x.Value())
// t.Logf("c: %3.3f", cost.Value())
// t.Logf("xG: %v", grads[0].Value())
h := NewGraph()
a := NewTensor(h, dt, 4, WithShape(2, 1, 28, 28), WithInit(RangedFrom(0)))
b, err := Im2Col(a, kernel, pad, stride, dilation)
if err != nil {
t.Error(err)
return
}
cost2 := Must(Sum(b))
n := NewLispMachine(h)
defer n.Close()
if err = n.RunAll(); err != nil {
t.Error(err)
return
}
aG, err := a.Grad()
if err != nil {
t.Error(err)
return
}
// t.Logf("a: %v", a.Value())
// t.Logf("c: %3.3f", cost2.Value())
// t.Logf("aG: %v", aG)
assert.Equal(x.Value().Data(), a.Value().Data())
assert.Equal(grads[0].Value().Data(), aG.Data())
assert.Equal(cost.Value().Data(), cost2.Value().Data())
}
func TestIm2Col(t *testing.T) {
// assert := assert.New(t)
dts := []tensor.Dtype{tensor.Float64, tensor.Float32}
for _, dt := range dts {
for _, i2ct := range im2colTests {
im2colTest(t, dt, i2ct.kernel, i2ct.pad, i2ct.stride, i2ct.dilation)
}
}
}
func TestMaxPool2D_asymmetricPadding(t *testing.T) {
assert := assert.New(t)
tsts := []struct {
inputT tensor.Tensor
outputT tensor.Tensor
padding []int
}{
{
inputT: tensor.New(tensor.WithShape(1, 3, 32, 32),
tensor.WithBacking([]float32{1.7640524, 0.4001572, 0.978738, 2.2408931, 1.867558, -0.9772779, 0.95008844, -0.1513572, -0.10321885, 0.41059852, 0.14404356, 1.4542735, 0.7610377, 0.121675014, 0.44386324, 0.33367434, 1.4940791, -0.20515826, 0.3130677, -0.85409576, -2.5529897, 0.6536186, 0.8644362, -0.742165, 2.2697546, -1.4543657, 0.045758516, -0.18718386, 1.5327792, 1.4693588, 0.15494743, 0.37816253, -0.88778573, -1.9807965, -0.34791216, 0.15634897, 1.2302907, 1.2023798, -0.3873268, -0.30230275, -1.048553, -1.420018, -1.7062702, 1.9507754, -0.5096522, -0.4380743, -1.2527953, 0.7774904, -1.6138978, -0.21274029, -0.89546657, 0.3869025, -0.51080513, -1.1806322, -0.028182229, 0.42833188, 0.06651722, 0.3024719, -0.6343221, -0.36274117, -0.67246044, -0.35955316, -0.8131463, -1.7262826, 0.17742614, -0.40178093, -1.6301984, 0.46278226, -0.9072984, 0.051945396, 0.7290906, 0.12898292, 1.1394007, -1.2348258, 0.40234163, -0.6848101, -0.87079716, -0.5788497, -0.31155252, 0.05616534, -1.1651498, 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1.1781894, 1.1781894, -0.94154567, 0.25471553, 0.25471553}),
),
},
}
for _, tst := range tsts {
g := NewGraph()
x := NodeFromAny(g, tst.inputT)
y, err := MaxPool2D(x, tensor.Shape{2, 2}, []int{1, 0, 1, 0}, []int{1, 1})
if err != nil {
t.Fatal(err)
}
m := NewTapeMachine(g, BindDualValues())
if err := m.RunAll(); err != nil {
t.Fatal(err)
}
m.Close()
if len(y.Shape()) != len(tst.outputT.Shape()) {
t.Fatalf("Maxpool: expected shape %v, got %v", tst.outputT.Shape(), y.Shape())
}
for i, v := range y.Shape() {
if v != tst.outputT.Shape()[i] {
t.Fatalf("Maxpool: expected shape %v, got %v", tst.outputT.Shape(), y.Shape())
}
}
assert.Equal(y.Value().Data(), tst.outputT.Data())
}
}
func TestMaxPool2D(t *testing.T) {
assert := assert.New(t)
dts := []tensor.Dtype{tensor.Float64, tensor.Float32}
for _, dt := range dts {
g := NewGraph()
x := NewTensor(g, dt, 4, WithShape(1, 2, 3, 4), WithInit(RangedFrom(0)))
y, err := MaxPool2D(x, tensor.Shape{2, 2}, []int{0, 0}, []int{1, 1})
if err != nil {
t.Fatal(err)
}
cost := Must(Sum(y))
grads, err := Grad(cost, x)
if err != nil {
t.Fatal(err)
}
m := NewTapeMachine(g, BindDualValues())
if err := m.RunAll(); err != nil {
t.Fatal(err)
}
// t.Logf("x %v", x.Value())
// t.Logf("y: %v", y.Value())
// t.Logf("c: %v", cost.Value())
// t.Logf("xG: %v", grads[0])
h := NewGraph()
a := NewTensor(h, dt, 4, WithShape(1, 2, 3, 4), WithInit(RangedFrom(0)))
b, err := MaxPool2D(a, tensor.Shape{2, 2}, []int{0, 0}, []int{1, 1})
if err != nil {
t.Fatal(err)
}
cost2 := Must(Sum(b))
if err != nil {
t.Fatal(err)
}
m2 := NewLispMachine(h)
if err = m2.RunAll(); err != nil {
t.Fatal(err)
}
aG, err := a.Grad()
if err != nil {
t.Error(err)
return
}
assert.Equal(x.Value().Data(), a.Value().Data())
assert.Equal(grads[0].Value().Data(), aG.Data())
assert.Equal(cost.Value().Data(), cost2.Value().Data())
m.Close()
m2.Close()
}
}
func TestMaxPool(t *testing.T) {
testCases := []struct {
desc string
input tensor.Tensor
kernelSize tensor.Shape
pad []int
stride []int
expectedOutput []float64
expectedShape tensor.Shape
expectedCost float64
PoolFunc func(*Node, tensor.Shape, []int, []int) (*Node, error)
}{
{
desc: "Example 1",
input: tensor.New(
tensor.WithShape(1, 1, 4, 4),
tensor.WithBacking(tensor.Range(tensor.Float64, 0, 16)),
),
kernelSize: []int{4, 4},
pad: []int{0, 0},
stride: []int{1, 1},
expectedOutput: []float64{15},
expectedCost: 196,
expectedShape: tensor.Shape{1, 1, 1, 1},
PoolFunc: MaxPool2D,
},
}
for _, tcase := range testCases {
t.Run(tcase.desc, func(t *testing.T) {
c := require.New(t)
g := NewGraph()
input := NewTensor(g, tensor.Float64, tcase.input.Shape().Dims(), WithName("input"), WithShape(tcase.input.Shape()...), WithValue(tcase.input))
output, err := tcase.PoolFunc(input, tcase.kernelSize, tcase.pad, tcase.stride)
c.NoError(err)
t.Logf("%v output shape: %v", tcase.desc, output.Shape())
t.Logf("%v input shape: %v", tcase.desc, input.Shape())
y := NewTensor(g, output.Dtype(), output.Dims(), WithShape(output.Shape()...), WithInit(Ones()))
cost := Must(Mean(Must(Square(Must(Sub(output, y)))))) // MSE
_, err = Grad(cost, input)
c.NoError(err)
// logger := log.New(os.Stdout, "", 0)
vm := NewTapeMachine(
g,
//WithLogger(logger),
WithWatchlist(),
BindDualValues(output),
TraceExec(),
)
c.NoError(vm.RunAll())
c.NoError(vm.Close())
t.Logf("%v input %v", tcase.desc, input.Value())
t.Logf("%v result: %v", tcase.desc, output.Value())
t.Logf("%v cost: %v", tcase.desc, cost.Value())
c.Equal(tcase.expectedOutput, output.Value().Data())
c.Equal(tcase.expectedShape, output.Shape())
c.Equal(tcase.expectedCost, cost.Value().Data().(float64))
})
}
}
var (
bnSetStatsCases = []struct {
Dtype tensor.Dtype
Init InitWFn
Shape tensor.Shape
expectedErr string
}{
{Float32, RangedFromWithStep(0.0, 0.1), tensor.Shape{2, 2}, "invalid runningMean shape (2, 2). Expected: (2)"},
{Float64, RangedFromWithStep(0.0, 0.1), tensor.Shape{2}, "invalid runningMean type float64. Expected: float32"},
{Float32, RangedFromWithStep(0.0, 0.1), tensor.Shape{2}, ""},
}
)
func TestBatchNormSetStats(t *testing.T) {
g := NewGraph()
x := NewTensor(g, Float32, 2, WithShape(2, 2), WithInit(Zeroes()), WithName("x"))
_, _, _, op, _ := BatchNorm(x, nil, nil, 0.9, 1e-5)
for i, tC := range bnSetStatsCases {
t.Run(fmt.Sprintf("Example %d (%v)", i+1, tC.Dtype), func(t *testing.T) {
c := assert.New(t)
backing := tC.Init(tC.Dtype, tC.Shape...)
err := op.SetStats(
tensor.New(
tensor.Of(tC.Dtype),
tensor.WithShape(tC.Shape...),
tensor.WithBacking(backing),
),
tensor.New(
tensor.Of(tC.Dtype),
tensor.WithShape(tC.Shape...),
tensor.WithBacking(backing),
),
)
if tC.expectedErr == "" {
c.NoError(err)
m, v := op.Stats()
c.EqualValues(backing, m.Data())
c.EqualValues(backing, v.Data())
} else {
c.EqualError(err, tC.expectedErr)
}
})
}
}
func TestBatchNormAll(t *testing.T) {
for i, tC := range bnAllCases {
t.Run(fmt.Sprintf("#%d %v", i+1, tC.desc), func(t *testing.T) {
rand.Seed(0)
c := require.New(t)
g := NewGraph()
var initOpt NodeConsOpt
switch v := tC.X.(type) {
case []float32:
initOpt = WithValue(
tensor.New(
tensor.Of(tensor.Float32),
tensor.WithShape(tC.XShape...),
tensor.WithBacking(v),
),
)
case []float64:
initOpt = WithValue(
tensor.New(
tensor.Of(tensor.Float32),
tensor.WithShape(tC.XShape...),
tensor.WithBacking(v),
),
)
case InitWFn:
initOpt = WithInit(v)
}
x := NewTensor(g, tC.Dtype, tC.XShape.Dims(), WithShape(tC.XShape...), initOpt, WithName("x"))
scale := NewTensor(g, tC.Dtype, tC.ScaleShape.Dims(), WithShape(tC.ScaleShape...), WithInit(tC.ScaleInit), WithName("scale"))
bias := NewTensor(g, tC.Dtype, tC.BiasShape.Dims(), WithShape(tC.BiasShape...), WithInit(tC.BiasInit), WithName("bias"))
fcWeight := NewTensor(g, tC.Dtype, 2, WithShape(tC.XShape[0], tensor.Shape(tC.XShape[1:]).TotalSize()), WithInit(tC.ScaleInit), WithName("fcWeight"))
y, _, _, op, err := BatchNorm(x, scale, bias, 0.9, 1e-5)
c.NoError(err)
if y.Dims() > 2 {
y = Must(Reshape(y, fcWeight.Shape()))
}
wT := Must(Transpose(fcWeight, 1, 0))
y = Must(Mul(y, wT))
var yVal, scaleVal Value
Read(y, &yVal)
Read(scale, &scaleVal)
cost, _ := Mean(y)
if _, err := Grad(cost, x, fcWeight, scale, bias); err != nil {
t.Fatal(err)
}
optimizer := NewAdamSolver(WithLearnRate(0.02))
m := NewTapeMachine(g, BindDualValues(x, fcWeight, scale, bias), TraceExec(), WithInfWatch(), WithNaNWatch())
err = m.RunAll()
c.NoError(err)
c.NoError(m.Close())
err = optimizer.Step(NodesToValueGrads(Nodes{fcWeight, scale, bias}))
c.NoError(err)
// for visual inspection
t.Logf("%v input:\n%v", tC.desc, x.Value())
t.Logf("%v running mean: %v", tC.desc, op.runningMean)
t.Logf("%v running var: %v", tC.desc, op.runningVariance)
t.Logf("%v output:\n%v", tC.desc, y.Value())
t.Logf("%v output grad:\n%v", tC.desc, y.Deriv().Value())
t.Logf("%v fc weight:\n%v", tC.desc, fcWeight.Value())
t.Logf("%v scale: %v", tC.desc, scale.Value())
t.Logf("%v scale grad: %v", tC.desc, scale.Deriv().Value())
t.Logf("%v bias: %v", tC.desc, bias.Value())
t.Logf("%v bias grad: %v", tC.desc, bias.Deriv().Value())
t.Logf("%v input grad:\n%v", tC.desc, x.Deriv().Value())
runningMean, runningVariance := op.Stats()
c.NotNil(runningMean)
c.NotNil(runningVariance)
c.True(dawson.AllClose(tC.ExpectedMean, runningMean.Data()), "Mean doesn't match:\ngot=%#v expected=%#v", op.runningMean.Data(), tC.ExpectedMean)
c.True(dawson.AllClose(tC.ExpectedVariance, runningVariance.Data()), "Var doesn't match:\ngot=%#v expected=%#v", op.runningVariance.Data(), tC.ExpectedVariance)
c.True(dawson.AllClose(tC.ExpectedTrainResult, yVal.Data()), "Wrong Output\ngot=%#v\nexpected=%#v", yVal.Data(), tC.ExpectedTrainResult)
c.True(dawson.AllClose(tC.ExpectedOutputGrad, y.Deriv().Value().Data()), "Output Grad doesn't match:\ngot=%#v expected=%#v", y.Deriv().Value().Data(), tC.ExpectedOutputGrad)
c.True(dawson.AllClose(tC.ExpectedBiasGrad, bias.Deriv().Value().Data()), "Bias Grad doesn't match:\ngot=%#v expected=%#v", bias.Deriv().Value().Data(), tC.ExpectedBiasGrad)
c.True(dawson.AllClose(tC.ExpectedScaleGrad, scale.Deriv().Value().Data()), "Scale Grad doens't match:\ngot=%#v expected=%#v", scale.Deriv().Value().Data(), tC.ExpectedScaleGrad)
c.True(dawson.AllClose(tC.ExpectedInputGrad, x.Deriv().Value().Data()), "Input Grad doesn't match:\ngot=%#v expected=%#v", x.Deriv().Value().Data(), tC.ExpectedInputGrad)
t.Logf("-------- Switching to Eval Mode --------")
m2 := NewTapeMachine(g, TraceExec(), WithNaNWatch(), WithInfWatch(), EvalMode())
err = m2.RunAll()
c.NoError(err)
c.NoError(m2.Close())
t.Logf("%v output:\n%v", tC.desc, yVal)
t.Logf("%v input grad:\n%v", tC.desc, x.Deriv().Value())
t.Logf("%v running mean: %v", tC.desc, op.runningMean)
t.Logf("%v running var: %v", tC.desc, op.runningVariance)
t.Logf("%v bias: %v", tC.desc, bias.Value())
t.Logf("%v bias grad: %v", tC.desc, bias.Deriv().Value())
t.Logf("%v scale: %v", tC.desc, scale.Value())
t.Logf("%v scale grad: %v", tC.desc, scale.Deriv().Value().Data())
c.True(dawson.AllClose(tC.ExpectedEvalResult, yVal.Data()), "Output doesn't match\ngot=%#v\nexpected=%#v", yVal.Data(), tC.ExpectedEvalResult)
})
}
}
func TestBatchNormStacked(t *testing.T) {
for _, tC := range bnstackedCases {
t.Run(tC.desc, func(t *testing.T) {
rand.Seed(0)
c := require.New(t)
g := NewGraph()
x := NewTensor(g, tC.Dtype, tC.XShape.Dims(), WithShape(tC.XShape...), WithInit(tC.XInit), WithName("x"))
t.Logf("%v input:\n%v", tC.desc, x.Value())
scale1 := NewTensor(g, tC.Dtype, tC.ScaleShape.Dims(), WithShape(tC.ScaleShape...), WithInit(tC.ScaleInit), WithName("scale1"))
bias1 := NewTensor(g, tC.Dtype, tC.BiasShape.Dims(), WithShape(tC.BiasShape...), WithInit(tC.BiasInit), WithName("bias1"))
y1, _, _, op1, err := BatchNorm(x, scale1, bias1, 0.9, 1e-5)
c.NoError(err)
op1.SetTraining(true)
scale2 := NewTensor(g, tC.Dtype, tC.ScaleShape.Dims(), WithShape(tC.ScaleShape...), WithInit(tC.ScaleInit), WithName("scale2"))
bias2 := NewTensor(g, tC.Dtype, tC.BiasShape.Dims(), WithShape(tC.BiasShape...), WithInit(tC.BiasInit), WithName("bias2"))
y2, _, _, op2, err := BatchNorm(y1, scale2, bias2, 0.9, 1e-5)
c.NoError(err)
op2.SetTraining(true)
var yVal, scaleVal Value
Read(y2, &yVal)
Read(scale2, &scaleVal)
cost := Must(Mean(y2))
if _, err := Grad(cost, x, scale1, bias1, scale2, bias2); err != nil {
t.Fatal(err)
}
m := NewTapeMachine(g, BindDualValues(x, scale1, bias1, scale2, bias2), TraceExec(), WithInfWatch())
optim := NewAdamSolver(WithLearnRate(0.05))
// optim := NewRMSPropSolver(WithLearnRate(0.05))
for i := 0; i < tC.Epochs; i++ {
t.Logf("-------- STEP %v", i+1)
op1.SetTraining(true)
op2.SetTraining(true)
err = m.RunAll()
c.NoError(err)
err = optim.Step(NodesToValueGrads(Nodes{
scale1,
bias1,
scale2,
bias2,
}))
c.NoError(err)
m.Reset()
}
c.NoError(err)
c.NoError(m.Close())
t.Logf("%v running mean: %v", tC.desc, op2.runningMean)
t.Logf("%v running var: %v", tC.desc, op2.runningVariance)
t.Logf("%v y1:\n%v", tC.desc, y1.Value())
t.Logf("%v output:\n%v", tC.desc, y2.Value())
t.Logf("%v output grad:\n%v", tC.desc, y2.Deriv().Value())
t.Logf("%v scale grad: %v", tC.desc, scale2.Deriv().Value())
t.Logf("%v bias grad: %v", tC.desc, bias2.Deriv().Value())
t.Logf("%v input grad:\n%v", tC.desc, x.Deriv().Value())
c.True(dawson.AllClose(tC.ExpectedMean, op2.runningMean.Data()), "Mean doesn't match:\ngot=%#v expected=%#v", op2.runningMean.Data(), tC.ExpectedMean)
c.True(dawson.AllClose(tC.ExpectedVariance, op2.runningVariance.Data()), "Var doesn't match:\ngot=%#v expected=%#v", op2.runningVariance.Data(), tC.ExpectedVariance)
c.True(dawson.AllClose(tC.ExpectedTrainResult, yVal.Data()), "Wrong Output\ngot=%#v\nexpected=%#v", yVal.Data(), tC.ExpectedTrainResult)
t.Logf("-------- Switching to Eval Mode --------")
m2 := NewTapeMachine(g, TraceExec(), WithInfWatch(), EvalMode())
err = m2.RunAll()
c.NoError(err)
c.NoError(m2.Close())
c.InDeltaSlice(tC.ExpectedEvalResult, yVal.Data(), 1e-5, "Output doesn't match\ngot=%#v\nexpected=%#v", yVal.Data(), tC.ExpectedEvalResult)
})
}
}
func TestLeakyRelu(t *testing.T) {
tests := []struct {
name string
alpha float64
xT tensor.Tensor
yT tensor.Tensor
}{
{
name: "simple float32",
alpha: 0.1,
xT: tensor.New(
tensor.WithShape(3, 4, 5),
tensor.WithBacking([]float32{1.7640524, 0.4001572, 0.978738, 2.2408931, 1.867558, -0.9772779, 0.95008844, -0.1513572, -0.10321885, 0.41059852, 0.14404356, 1.4542735, 0.7610377, 0.121675014, 0.44386324, 0.33367434, 1.4940791, -0.20515826, 0.3130677, -0.85409576, -2.5529897, 0.6536186, 0.8644362, -0.742165, 2.2697546, -1.4543657, 0.045758516, -0.18718386, 1.5327792, 1.4693588, 0.15494743, 0.37816253, -0.88778573, -1.9807965, -0.34791216, 0.15634897, 1.2302907, 1.2023798, -0.3873268, -0.30230275, -1.048553, -1.420018, -1.7062702, 1.9507754, -0.5096522, -0.4380743, -1.2527953, 0.7774904, -1.6138978, -0.21274029, -0.89546657, 0.3869025, -0.51080513, -1.1806322, -0.028182229, 0.42833188, 0.06651722, 0.3024719, -0.6343221, -0.36274117}),
),
yT: tensor.New(
tensor.WithShape(3, 4, 5),
tensor.WithBacking([]float32{1.7640524, 0.4001572, 0.978738, 2.2408931, 1.867558, -0.09772779, 0.95008844, -0.01513572, -0.010321885, 0.41059852, 0.14404356, 1.4542735, 0.7610377, 0.121675014, 0.44386324, 0.33367434, 1.4940791, -0.020515827, 0.3130677, -0.085409574, -0.25529897, 0.6536186, 0.8644362, -0.07421651, 2.2697546, -0.14543657, 0.045758516, -0.018718386, 1.5327792, 1.4693588, 0.15494743, 0.37816253, -0.08877858, -0.19807965, -0.034791216, 0.15634897, 1.2302907, 1.2023798, -0.03873268, -0.030230274, -0.1048553, -0.1420018, -0.17062703, 1.9507754, -0.05096522, -0.04380743, -0.12527953, 0.7774904, -0.16138978, -0.021274028, -0.08954666, 0.3869025, -0.051080514, -0.11806323, -0.0028182229, 0.42833188, 0.06651722, 0.3024719, -0.06343221, -0.036274116}),
),
},
{
name: "simple float64",
alpha: 0.1,
xT: tensor.New(
tensor.WithShape(3, 4, 5),
tensor.WithBacking([]float64{1.7640524, 0.4001572, 0.978738, 2.2408931, 1.867558, -0.9772779, 0.95008844, -0.1513572, -0.10321885, 0.41059852, 0.14404356, 1.4542735, 0.7610377, 0.121675014, 0.44386324, 0.33367434, 1.4940791, -0.20515826, 0.3130677, -0.85409576, -2.5529897, 0.6536186, 0.8644362, -0.742165, 2.2697546, -1.4543657, 0.045758516, -0.18718386, 1.5327792, 1.4693588, 0.15494743, 0.37816253, -0.88778573, -1.9807965, -0.34791216, 0.15634897, 1.2302907, 1.2023798, -0.3873268, -0.30230275, -1.048553, -1.420018, -1.7062702, 1.9507754, -0.5096522, -0.4380743, -1.2527953, 0.7774904, -1.6138978, -0.21274029, -0.89546657, 0.3869025, -0.51080513, -1.1806322, -0.028182229, 0.42833188, 0.06651722, 0.3024719, -0.6343221, -0.36274117}),
),
yT: tensor.New(
tensor.WithShape(3, 4, 5),
tensor.WithBacking([]float64{1.7640524, 0.4001572, 0.978738, 2.2408931, 1.867558, -0.09772779, 0.95008844, -0.01513572, -0.010321885, 0.41059852, 0.14404356, 1.4542735, 0.7610377, 0.121675014, 0.44386324, 0.33367434, 1.4940791, -0.020515827, 0.3130677, -0.085409574, -0.25529897, 0.6536186, 0.8644362, -0.07421651, 2.2697546, -0.14543657, 0.045758516, -0.018718386, 1.5327792, 1.4693588, 0.15494743, 0.37816253, -0.08877858, -0.19807965, -0.034791216, 0.15634897, 1.2302907, 1.2023798, -0.03873268, -0.030230274, -0.1048553, -0.1420018, -0.17062703, 1.9507754, -0.05096522, -0.04380743, -0.12527953, 0.7774904, -0.16138978, -0.021274028, -0.08954666, 0.3869025, -0.051080514, -0.11806323, -0.0028182229, 0.42833188, 0.06651722, 0.3024719, -0.06343221, -0.036274116}),
),
},
}
for _, tst := range tests {
t.Run(tst.name, func(t *testing.T) {
g := NewGraph()
assert := assert.New(t)
x := NodeFromAny(g, tst.xT)
output, err := LeakyRelu(x, tst.alpha)
if err != nil {
t.Fatalf("%+v", err)
}
m := NewTapeMachine(g)
if err := m.RunAll(); err != nil {
t.Fatalf("%+v", err)
}
defer m.Close()
assert.InDeltaSlice(tst.yT.Data(), output.Value().Data(), 1e-6, "the two tensors should be equal.")
})
}
}
func TestGlobalAveragePool2D_fwdPass(t *testing.T) {
for _, tst := range []struct {
inputT tensor.Tensor
expectedOutput tensor.Tensor
}{
{
inputT: tensor.New(
tensor.WithShape(1, 3, 5, 5),
tensor.WithBacking([]float32{
1.7640524, 0.4001572, 0.978738, 2.2408931, 1.867558,
-0.9772779, 0.95008844, -0.1513572, -0.10321885, 0.41059852,
0.14404356, 1.4542735, 0.7610377, 0.121675014, 0.44386324,
0.33367434, 1.4940791, -0.20515826, 0.3130677, -0.85409576,
-2.5529897, 0.6536186, 0.8644362, -0.742165, 2.2697546,
-1.4543657, 0.045758516, -0.18718386, 1.5327792, 1.4693588,
0.15494743, 0.37816253, -0.88778573, -1.9807965, -0.34791216,
0.15634897, 1.2302907, 1.2023798, -0.3873268, -0.30230275,
-1.048553, -1.420018, -1.7062702, 1.9507754, -0.5096522,
-0.4380743, -1.2527953, 0.7774904, -1.6138978, -0.21274029,
-0.89546657, 0.3869025, -0.51080513, -1.1806322, -0.028182229,
0.42833188, 0.06651722, 0.3024719, -0.6343221, -0.36274117,
-0.67246044, -0.35955316, -0.8131463, -1.7262826, 0.17742614,
-0.40178093, -1.6301984, 0.46278226, -0.9072984, 0.051945396,
0.7290906, 0.12898292, 1.1394007, -1.2348258, 0.40234163})),
expectedOutput: tensor.New(
tensor.WithShape(1, 3, 1, 1),
tensor.WithBacking([]float32{0.47517386, -0.1940553, -0.28326008})),
},
{
inputT: tensor.New(
tensor.WithShape(1, 3, 5, 5),
tensor.WithBacking([]float64{
1.7640524, 0.4001572, 0.978738, 2.2408931, 1.867558,
-0.9772779, 0.95008844, -0.1513572, -0.10321885, 0.41059852,
0.14404356, 1.4542735, 0.7610377, 0.121675014, 0.44386324,
0.33367434, 1.4940791, -0.20515826, 0.3130677, -0.85409576,
-2.5529897, 0.6536186, 0.8644362, -0.742165, 2.2697546,
-1.4543657, 0.045758516, -0.18718386, 1.5327792, 1.4693588,
0.15494743, 0.37816253, -0.88778573, -1.9807965, -0.34791216,
0.15634897, 1.2302907, 1.2023798, -0.3873268, -0.30230275,
-1.048553, -1.420018, -1.7062702, 1.9507754, -0.5096522,
-0.4380743, -1.2527953, 0.7774904, -1.6138978, -0.21274029,
-0.89546657, 0.3869025, -0.51080513, -1.1806322, -0.028182229,
0.42833188, 0.06651722, 0.3024719, -0.6343221, -0.36274117,
-0.67246044, -0.35955316, -0.8131463, -1.7262826, 0.17742614,
-0.40178093, -1.6301984, 0.46278226, -0.9072984, 0.051945396,
0.7290906, 0.12898292, 1.1394007, -1.2348258, 0.40234163})),
expectedOutput: tensor.New(
tensor.WithShape(1, 3, 1, 1),
tensor.WithBacking([]float64{0.47517386, -0.1940553, -0.28326008})),
},
} {
inputT := tst.inputT
expectedOutput := tst.expectedOutput
g := NewGraph()
assert := assert.New(t)
x := NodeFromAny(g, inputT)
output, err := GlobalAveragePool2D(x)
if err != nil {
t.Fatal(err)
}
m := NewTapeMachine(g)
if err := m.RunAll(); err != nil {
t.Fatalf("%+v", err)
}
defer m.Close()
if len(output.Shape()) != len(expectedOutput.Shape()) {
t.Fatalf("Bad output shape, expected %v, got %v", expectedOutput.Shape(), output.Shape())
}
for i, d := range output.Shape() {
if expectedOutput.Shape()[i] != d {
t.Fatalf("Bad output shape, expected %v, got %v", expectedOutput.Shape(), output.Shape())
}
}
assert.InDeltaSlice(expectedOutput.Data(), output.Value().Data(), 1e-6, "the two tensors should be equal.")
}
}
func TestConv2dErrors(t *testing.T) {
g := NewGraph()
testCases := []struct {
desc string
im *Node
filter *Node
kernelShape tensor.Shape
pad, stride, dilation []int
err string
panics bool
}{
{
desc: "Succesful",
im: NewTensor(g, tensor.Float64, 4, WithShape(1, 1, 28, 28)),
filter: NewTensor(g, tensor.Float64, 4, WithShape(32, 1, 3, 3)),
kernelShape: tensor.Shape{3, 3},
pad: []int{1, 1},
stride: []int{1, 1},
dilation: []int{1, 1},
err: "",
},
{
desc: "5dIM",
im: NewTensor(g, tensor.Float64, 5, WithShape(1, 1, 1, 28, 28)),
filter: NewTensor(g, tensor.Float64, 4, WithShape(32, 1, 3, 3)),
kernelShape: tensor.Shape{3, 3},
pad: []int{1, 1},
stride: []int{1, 1},
dilation: []int{1, 1},
err: "im should have 4 dims, got 5 dims",
},
{
desc: "5dFilter",
im: NewTensor(g, tensor.Float64, 4, WithShape(1, 1, 28, 28)),
filter: NewTensor(g, tensor.Float64, 5, WithShape(32, 1, 1, 3, 3)),
kernelShape: tensor.Shape{3, 3},
pad: []int{1, 1},
stride: []int{1, 1},
dilation: []int{1, 1},
err: "filter should have 4 dims, got 5 dims",
},
{
desc: "Shapes",
im: NewTensor(g, tensor.Float64, 4, WithShape(1, 1, 28, 28)),
filter: NewTensor(g, tensor.Float64, 4, WithShape(32, 3, 3, 3)),
kernelShape: tensor.Shape{3, 3},
pad: []int{1, 1},
stride: []int{1, 1},
dilation: []int{1, 1},
err: "3 (kernel) * 3 (width) * 3 (height) must be 9, got 27",
},
}
for _, tC := range testCases {
t.Run(tC.desc, func(t *testing.T) {
c := assert.New(t)
_, err := Conv2d(tC.im, tC.filter, tC.kernelShape, tC.pad, tC.stride, tC.dilation)
if tC.err != "" {
require.Error(t, err)
c.Equal(tC.err, err.Error())
} else {
c.NoError(err)
}
})
}
}