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More work on the torch version

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This commit is contained in:
Andre Henriques
2024-04-23 11:54:30 +01:00
parent a4a9ade71f
commit 527b57a111
4 changed files with 109 additions and 102 deletions
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123
test.go
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@@ -5,108 +5,117 @@ import (
dbtypes "git.andr3h3nriqu3s.com/andr3/fyp/logic/db_types"
"git.andr3h3nriqu3s.com/andr3/fyp/logic/models/train/torch"
my_nn "git.andr3h3nriqu3s.com/andr3/fyp/logic/models/train/torch/nn"
torch "git.andr3h3nriqu3s.com/andr3/gotch/ts"
"github.com/charmbracelet/log"
)
func _main() {
func main() {
log.Info("Hello world")
m := train.BuildModel([]*dbtypes.Layer{
&dbtypes.Layer{
LayerType: dbtypes.LAYER_INPUT,
Shape: "[ 2, 3, 3 ]",
Shape: "[ 3, 28, 28 ]",
},
&dbtypes.Layer{
LayerType: dbtypes.LAYER_FLATTEN,
},
&dbtypes.Layer{
LayerType: dbtypes.LAYER_DENSE,
Shape: "[ 10 ]",
Shape: "[ 27 ]",
},
&dbtypes.Layer{
LayerType: dbtypes.LAYER_DENSE,
Shape: "[ 10 ]",
Shape: "[ 18 ]",
},
// &dbtypes.Layer{
// LayerType: dbtypes.LAYER_DENSE,
// Shape: "[ 9 ]",
// },
}, 0, true)
var err error
//var err error
d := gotch.CudaIfAvailable()
log.Info("device", "d", d)
log.Info("device", "d", d)
m.To(d)
m.To(d)
var ones_grad float64 = 0
var count = 0
vars1 := m.Vs.Variables()
for k, v := range vars1 {
ones := torch.MustOnes(v.MustSize(), gotch.Float, d)
v := ones.MustSetRequiresGrad(true, false)
v.MustDrop()
ones.RetainGrad(false)
m.Vs.UpdateVarTensor(k, ones, true)
m.Refresh()
}
opt, err := my_nn.DefaultAdamConfig().Build(m.Vs, 0.001)
if err != nil {
return
}
ones := torch.MustOnes([]int64{1, 2, 3, 3}, gotch.Float, d)
ones = ones.MustSetRequiresGrad(true, true)
ones.RetainGrad(false)
log.Info("start")
res := m.ForwardT(ones, true)
res = res.MustSetRequiresGrad(true, true)
res.RetainGrad(false)
for count < 100 {
outs := torch.MustOnes([]int64{1, 10}, gotch.Float, d)
outs = outs.MustSetRequiresGrad(true, true)
outs.RetainsGrad(false)
ones := torch.MustOnes([]int64{1, 3, 28, 28}, gotch.Float, d)
// ones = ones.MustSetRequiresGrad(true, true)
// ones.RetainGrad(false)
res := m.ForwardT(ones, true)
res = res.MustSetRequiresGrad(true, true)
res.RetainGrad(false)
loss, err := res.BinaryCrossEntropyWithLogits(outs, &torch.Tensor{}, &torch.Tensor{}, 1, false)
if err != nil {
return
}
loss = loss.MustSetRequiresGrad(true, false)
outs := torch.MustZeros([]int64{1, 18}, gotch.Float, d)
opt.ZeroGrad()
log.Info("loss", "loss", loss.Float64Values())
loss.MustBackward()
opt.Step()
// log.Info(mean.MustGrad(false).Float64Values())
log.Info(res.MustGrad(false).Float64Values())
log.Info(ones.MustGrad(false).Float64Values())
log.Info(outs.MustGrad(false).Float64Values())
vars := m.Vs.Variables()
for k, v := range vars {
log.Info("[grad check]", "k", k)
var grad *torch.Tensor
grad, err = v.Grad(false)
loss, err := res.BinaryCrossEntropyWithLogits(outs, &torch.Tensor{}, &torch.Tensor{}, 2, false)
if err != nil {
log.Error(err)
return
log.Fatal(err)
}
loss = loss.MustSetRequiresGrad(true, true)
opt.ZeroGrad()
log.Info("loss", "loss", loss.Float64Values())
loss.MustBackward()
opt.Step()
// log.Info(mean.MustGrad(false).Float64Values())
//ones_grad = ones.MustGrad(true).MustMax(true).Float64Values()[0]
log.Info(res.MustGrad(true).MustMax(true).Float64Values())
log.Info(ones_grad)
vars := m.Vs.Variables()
for k, v := range vars {
log.Info("[grad check]", "k", k, "grad", v.MustGrad(false).MustMax(true).Float64Values())
}
grad, err = grad.Abs(false)
if err != nil {
log.Error(err)
return
}
m.Debug()
grad, err = grad.Max(false)
if err != nil {
log.Error(err)
return
}
outs.MustDrop()
count += 1
log.Fatal("grad zero")
log.Info("[grad check]", "k", k, "grad", grad.Float64Values())
}
log.Warn("out")
}