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corrected Int64Option and Float64Option param functions

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This commit is contained in:
sugarme 2020-11-02 22:35:25 +11:00
parent b2bc72b1d4
commit c38c909977
16 changed files with 16457 additions and 19149 deletions
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6
example/char-rnn/main.go
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@ -116,9 +116,9 @@ func main() {
sumLoss += loss.Float64Values()[0]
cntLoss += 1.0
batchTs.MustDrop()
batchNarrow.MustDrop()
xsOnehotTmp.MustDrop()
// batchTs.MustDrop()
// batchNarrow.MustDrop()
// xsOnehotTmp.MustDrop()
xsOnehot.MustDrop()
ys.MustDrop()
lstmOut.MustDrop()

8
example/mnist/cnn.go
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@ -117,21 +117,21 @@ func runCNN1() {
logits := net.ForwardT(bImages, true)
loss := logits.CrossEntropyForLogits(bLabels)
// loss = loss.MustSetRequiresGrad(true)
// loss = loss.MustSetRequiresGrad(true, false)
opt.BackwardStep(loss)
epocLoss = loss.MustShallowClone()
epocLoss.Detach_()
// fmt.Printf("completed \t %v batches\t %.2f\n", i, loss.Values()[0])
// fmt.Printf("completed \t %v batches\t %.2f\n", i, loss.Float64Values()[0])
bImages.MustDrop()
bLabels.MustDrop()
}
vs.Freeze()
// vs.Freeze()
testAccuracy := nn.BatchAccuracyForLogits(vs, net, testImages, testLabels, vs.Device(), 1024)
vs.Unfreeze()
// vs.Unfreeze()
fmt.Printf("Epoch: %v\t Loss: %.2f \t Test accuracy: %.2f%%\n", epoch, epocLoss.Float64Values()[0], testAccuracy*100.0)
if testAccuracy > bestAccuracy {
bestAccuracy = testAccuracy

2
example/mnist/linear.go
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@ -45,7 +45,7 @@ func runLinear() {
})
testLogits := ds.TestImages.MustMm(ws, false).MustAdd(bs, true)
testAccuracy := testLogits.MustArgmax(-1, false, true).MustEq1(ds.TestLabels, true).MustTotype(gotch.Float, true).MustMean(gotch.Float, true).MustView([]int64{-1}, true).MustFloat64Value([]int64{0})
testAccuracy := testLogits.MustArgmax([]int64{-1}, false, true).MustEq1(ds.TestLabels, true).MustTotype(gotch.Float, true).MustMean(gotch.Float, true).MustView([]int64{-1}, true).MustFloat64Value([]int64{0})
fmt.Printf("Epoch: %v - Loss: %.3f - Test accuracy: %.2f%%\n", epoch, loss.Float64Values()[0], testAccuracy*100)

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example/tensor-io/file.pt
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example/tensor-io/file_multi.pt
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example/tensor-io/mnist-tensor-saved.png
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2
example/yolo/darknet.go
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@ -271,7 +271,7 @@ func upsample(prevChannels int64) (retVal1 int64, retVal2 interface{}) {
h := res[2]
w := res[3]
return xs.MustUpsampleNearest2d([]int64{h * 2, w * 2}, 2.0, 2.0, false)
return xs.MustUpsampleNearest2d([]int64{h * 2, w * 2}, []float64{2.0}, []float64{2.0}, false)
})
return prevChannels, Layer{Val: layer}

22
gen/gen.ml
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@ -742,7 +742,16 @@ let write_wrapper funcs filename =
; "Split"
; "SplitWithSizes"
; "Unbind"
; "Where" ]
; "Where"
; "Atleast1d1"
; "Atleast2d1"
; "Atleast3d1"
; "Dequantize1"
; "QuantizePerTensor1"
; "UnsafeChunk"
; "UnsafeSplit"
; "UnsafeSplitWithSizes"
; "AlignTensors" ]
in
if
List.exists excluded_funcs ~f:(fun name ->
@ -848,7 +857,16 @@ let write_must_wrapper funcs filename =
; "Split"
; "SplitWithSizes"
; "Unbind"
; "Where" ]
; "Where"
; "Atleast1d1"
; "Atleast2d1"
; "Atleast3d1"
; "Dequantize1"
; "QuantizePerTensor1"
; "UnsafeChunk"
; "UnsafeSplit"
; "UnsafeSplitWithSizes"
; "AlignTensors" ]
in
if
List.exists excluded_funcs ~f:(fun name ->

12431
libtch/c-generated.go
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File diff suppressed because it is too large Load Diff

34
nn/sequential.go
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@ -254,6 +254,40 @@ func BatchAccuracyForLogits(vs *VarStore, m ts.ModuleT, xs, ys *ts.Tensor, d got
return sumAccuracy / sampleCount
}
func BatchAccuracyForLogitsOld(vs *VarStore, m ts.ModuleT, xs, ys *ts.Tensor, d gotch.Device, batchSize int) (retVal float64) {
var (
sumAccuracy float64 = 0.0
sampleCount float64 = 0.0
)
vs.Freeze()
defer vs.Unfreeze()
iter2 := ts.MustNewIter2(xs, ys, int64(batchSize))
for {
item, ok := iter2.Next()
if !ok {
break
}
size := float64(item.Data.MustSize()[0])
bImages := item.Data.MustTo(d, true)
bLabels := item.Label.MustTo(d, true)
logits := m.ForwardT(bImages, false)
acc := logits.AccuracyForLogits(bLabels)
sumAccuracy += acc.Float64Values()[0] * size
sampleCount += size
bImages.MustDrop()
bLabels.MustDrop()
acc.MustDrop()
}
return sumAccuracy / sampleCount
}
// BatchAccuracyForLogitIdx is an alternative of BatchAccuracyForLogits to
// calculate accuracy for specified batch on module weight. It uses tensor
// indexing instead of Iter2

22964
tensor/must-tensor-generated.go
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File diff suppressed because it is too large Load Diff

2
tensor/other.go
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@ -19,7 +19,7 @@ func (ts *Tensor) CrossEntropyForLogits(targets *Tensor) (retVal *Tensor) {
// AccuracyForLogits returns the average accuracy for some given logits assuming that
// targets represent ground-truth.
func (ts *Tensor) AccuracyForLogits(targets *Tensor) (retVal *Tensor) {
argmax := ts.MustArgmax(-1, false, true)
argmax := ts.MustArgmax([]int64{-1}, false, true)
eq1 := argmax.MustEq1(targets, true)
return eq1.MustTotype(gotch.Float, true).MustMean(gotch.Float, true)
}

125
tensor/tensor-generated.go
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@ -3579,22 +3579,6 @@ func (ts *Tensor) Atleast1d(del bool) (retVal *Tensor, err error) {
return retVal, err
}
func Atleast1d1(tensors []Tensor) (retVal []Tensor, err error) {
ptr := (*lib.Ctensor)(unsafe.Pointer(C.malloc(0)))
var ctensors []lib.Ctensor
for _, t := range tensors {
ctensors = append(ctensors, t.ctensor)
}
lib.AtgAtleast1d1(ptr, ctensors, len(ctensors))
if err = TorchErr(); err != nil {
return retVal, err
}
retVal = &Tensor{ctensor: *ptr}
return retVal, err
}
func (ts *Tensor) Atleast2d(del bool) (retVal *Tensor, err error) {
if del {
defer ts.MustDrop()
@ -3610,22 +3594,6 @@ func (ts *Tensor) Atleast2d(del bool) (retVal *Tensor, err error) {
return retVal, err
}
func Atleast2d1(tensors []Tensor) (retVal []Tensor, err error) {
ptr := (*lib.Ctensor)(unsafe.Pointer(C.malloc(0)))
var ctensors []lib.Ctensor
for _, t := range tensors {
ctensors = append(ctensors, t.ctensor)
}
lib.AtgAtleast2d1(ptr, ctensors, len(ctensors))
if err = TorchErr(); err != nil {
return retVal, err
}
retVal = &Tensor{ctensor: *ptr}
return retVal, err
}
func (ts *Tensor) Atleast3d(del bool) (retVal *Tensor, err error) {
if del {
defer ts.MustDrop()
@ -3641,22 +3609,6 @@ func (ts *Tensor) Atleast3d(del bool) (retVal *Tensor, err error) {
return retVal, err
}
func Atleast3d1(tensors []Tensor) (retVal []Tensor, err error) {
ptr := (*lib.Ctensor)(unsafe.Pointer(C.malloc(0)))
var ctensors []lib.Ctensor
for _, t := range tensors {
ctensors = append(ctensors, t.ctensor)
}
lib.AtgAtleast3d1(ptr, ctensors, len(ctensors))
if err = TorchErr(); err != nil {
return retVal, err
}
retVal = &Tensor{ctensor: *ptr}
return retVal, err
}
func (ts *Tensor) AvgPool1d(kernelSize []int64, stride []int64, padding []int64, ceilMode bool, countIncludePad bool, del bool) (retVal *Tensor, err error) {
if del {
defer ts.MustDrop()
@ -6077,22 +6029,6 @@ func (ts *Tensor) Dequantize(del bool) (retVal *Tensor, err error) {
return retVal, err
}
func Dequantize1(tensors []Tensor) (retVal []Tensor, err error) {
ptr := (*lib.Ctensor)(unsafe.Pointer(C.malloc(0)))
var ctensors []lib.Ctensor
for _, t := range tensors {
ctensors = append(ctensors, t.ctensor)
}
lib.AtgDequantize1(ptr, ctensors, len(ctensors))
if err = TorchErr(); err != nil {
return retVal, err
}
retVal = &Tensor{ctensor: *ptr}
return retVal, err
}
func (ts *Tensor) Det(del bool) (retVal *Tensor, err error) {
if del {
defer ts.MustDrop()
@ -14649,22 +14585,6 @@ func (ts *Tensor) QuantizePerTensor(scale float64, zeroPoint int64, dtype gotch.
return retVal, err
}
func QuantizePerTensor1(tensors []Tensor, scales *Tensor, zeroPoints *Tensor, dtype gotch.DType) (retVal []Tensor, err error) {
ptr := (*lib.Ctensor)(unsafe.Pointer(C.malloc(0)))
var ctensors []lib.Ctensor
for _, t := range tensors {
ctensors = append(ctensors, t.ctensor)
}
lib.AtgQuantizePerTensor1(ptr, ctensors, len(ctensors), scales.ctensor, zeroPoints.ctensor, dtype.CInt())
if err = TorchErr(); err != nil {
return retVal, err
}
retVal = &Tensor{ctensor: *ptr}
return retVal, err
}
func QuantizedBatchNorm(input *Tensor, weight *Tensor, bias *Tensor, mean *Tensor, vari *Tensor, eps float64, outputScale float64, outputZeroPoint int64) (retVal *Tensor, err error) {
ptr := (*lib.Ctensor)(unsafe.Pointer(C.malloc(0)))
@ -18377,51 +18297,6 @@ func (ts *Tensor) Uniform_(from float64, to float64) (err error) {
return err
}
func (ts *Tensor) UnsafeChunk(chunks int64, dim int64, del bool) (retVal []Tensor, err error) {
if del {
defer ts.MustDrop()
}
ptr := (*lib.Ctensor)(unsafe.Pointer(C.malloc(0)))
lib.AtgUnsafeChunk(ptr, ts.ctensor, chunks, dim)
if err = TorchErr(); err != nil {
return retVal, err
}
retVal = &Tensor{ctensor: *ptr}
return retVal, err
}
func (ts *Tensor) UnsafeSplit(splitSize int64, dim int64, del bool) (retVal []Tensor, err error) {
if del {
defer ts.MustDrop()
}
ptr := (*lib.Ctensor)(unsafe.Pointer(C.malloc(0)))
lib.AtgUnsafeSplit(ptr, ts.ctensor, splitSize, dim)
if err = TorchErr(); err != nil {
return retVal, err
}
retVal = &Tensor{ctensor: *ptr}
return retVal, err
}
func (ts *Tensor) UnsafeSplitWithSizes(splitSizes []int64, dim int64, del bool) (retVal []Tensor, err error) {
if del {
defer ts.MustDrop()
}
ptr := (*lib.Ctensor)(unsafe.Pointer(C.malloc(0)))
lib.AtgUnsafeSplitWithSizes(ptr, ts.ctensor, splitSizes, len(splitSizes), dim)
if err = TorchErr(); err != nil {
return retVal, err
}
retVal = &Tensor{ctensor: *ptr}
return retVal, err
}
func (ts *Tensor) Unsqueeze(dim int64, del bool) (retVal *Tensor, err error) {
if del {
defer ts.MustDrop()

2
tensor/tensor.go
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@ -1171,7 +1171,7 @@ func (ts *Tensor) Swish() *Tensor {
}
func (ts *Tensor) AvgPool2DDefault(ksize int64, del bool) *Tensor {
return ts.MustAvgPool2d([]int64{ksize, ksize}, []int64{ksize, ksize}, []int64{0, 0}, false, true, 1, del)
return ts.MustAvgPool2d([]int64{ksize, ksize}, []int64{ksize, ksize}, []int64{0, 0}, false, true, []int64{1}, del)
}
// SaveMultiNew saves a slice of named tensors to the given file path.

2
vision/densenet.go
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@ -103,7 +103,7 @@ func densenet(p *nn.Path, cIn, cOut, bnSize int64, blockConfig []int64, growth i
seq.AddFn(nn.NewFunc(func(xs *ts.Tensor) *ts.Tensor {
tmp1 := xs.MustRelu(false)
tmp2 := tmp1.MustAvgPool2d([]int64{7, 7}, []int64{1, 1}, []int64{0, 0}, false, true, 1, true)
tmp2 := tmp1.MustAvgPool2d([]int64{7, 7}, []int64{1, 1}, []int64{0, 0}, false, true, []int64{1}, true)
res := tmp2.FlatView()
tmp2.MustDrop()
return res

6
vision/inception.go
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@ -78,7 +78,7 @@ func inceptionA(p *nn.Path, cIn, cPool int64) ts.ModuleT {
b3Ts := b3Tmp2.ApplyT(b33, train)
b3Tmp2.MustDrop()
bpoolTmp := xs.MustAvgPool2d([]int64{3, 3}, []int64{1, 1}, []int64{1, 1}, false, true, 9, false)
bpoolTmp := xs.MustAvgPool2d([]int64{3, 3}, []int64{1, 1}, []int64{1, 1}, false, true, []int64{9}, false)
bpoolTs := bpoolTmp.ApplyT(bpool, train)
res := ts.MustCat([]ts.Tensor{*b1Ts, *b2Ts, *b3Ts, *bpoolTs}, 1)
@ -145,7 +145,7 @@ func inceptionC(p *nn.Path, cIn int64, c7 int64) ts.ModuleT {
b3Ts := b3Tmp4.ApplyT(b35, train)
b3Tmp4.MustDrop()
bpTmp1 := xs.MustAvgPool2d([]int64{3, 3}, []int64{1, 1}, []int64{1, 1}, false, true, 9, false)
bpTmp1 := xs.MustAvgPool2d([]int64{3, 3}, []int64{1, 1}, []int64{1, 1}, false, true, []int64{9}, false)
bpoolTs := bpTmp1.ApplyT(bpool, train)
return ts.MustCat([]ts.Tensor{*b1Ts, *b2Ts, *b3Ts, *bpoolTs}, 1)
@ -211,7 +211,7 @@ func inceptionE(p *nn.Path, cIn int64) ts.ModuleT {
b3bTs := b3Tmp2.ApplyT(b33b, train)
b3Ts := ts.MustCat([]ts.Tensor{*b3aTs, *b3bTs}, 1)
bpTmp1 := xs.MustAvgPool2d([]int64{3, 3}, []int64{1, 1}, []int64{1, 1}, false, true, 9, false)
bpTmp1 := xs.MustAvgPool2d([]int64{3, 3}, []int64{1, 1}, []int64{1, 1}, false, true, []int64{9}, false)
bpoolTs := bpTmp1.ApplyT(bpool, train)
return ts.MustCat([]ts.Tensor{*b1Ts, *b2Ts, *b3Ts, *bpoolTs}, 1)