feat(example/mnist): conv
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sugarme
parent
b792c6af3c
commit
3e08ff3a41
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@ -3,6 +3,7 @@ package main
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import (
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"fmt"
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"log"
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"time"
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"github.com/sugarme/gotch"
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"github.com/sugarme/gotch/nn"
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@ -13,8 +14,9 @@ import (
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const (
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MnistDirCNN string = "../../data/mnist"
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epochsCNN = 10
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epochsCNN = 100
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batchCNN = 256
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batchSize = 256
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LrCNN = 1e-4
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)
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@ -39,20 +41,47 @@ func newNet(vs *nn.Path) Net {
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*fc2}
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}
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func (n Net) ForwardT(xs ts.Tensor, train bool) ts.Tensor {
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out := xs.MustView([]int64{-1, 1, 28, 28}).Apply(n.conv1).MaxPool2DDefault(2, true)
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out = out.Apply(n.conv2).MaxPool2DDefault(2, true)
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out = out.MustView([]int64{-1, 1024}).Apply(&n.fc1).MustRelu(true)
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out.Dropout_(0.5, train)
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return out.Apply(&n.fc2)
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func (n Net) ForwardT(xs ts.Tensor, train bool) (retVal ts.Tensor) {
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outView1 := xs.MustView([]int64{-1, 1, 28, 28}, false)
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defer outView1.MustDrop()
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outC1 := outView1.Apply(n.conv1)
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// defer outC1.MustDrop()
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outMP1 := outC1.MaxPool2DDefault(2, true)
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defer outMP1.MustDrop()
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outC2 := outMP1.Apply(n.conv2)
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// defer outC2.MustDrop()
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outMP2 := outC2.MaxPool2DDefault(2, true)
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// defer outMP2.MustDrop()
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outView2 := outMP2.MustView([]int64{-1, 1024}, true)
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defer outView2.MustDrop()
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outFC1 := outView2.Apply(&n.fc1)
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// defer outFC1.MustDrop()
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outRelu := outFC1.MustRelu(true)
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defer outRelu.MustDrop()
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// outRelu.Dropout_(0.5, train)
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outDropout := ts.MustDropout(outRelu, 0.5, train)
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defer outDropout.MustDrop()
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return outDropout.Apply(&n.fc2)
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}
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func runCNN() {
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var ds vision.Dataset
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ds = vision.LoadMNISTDir(MnistDirNN)
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// testImages := ds.TestImages
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// testLabels := ds.TestLabels
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cuda := gotch.CudaBuilder(0)
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vs := nn.NewVarStore(cuda.CudaIfAvailable())
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// vs := nn.NewVarStore(gotch.CPU)
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path := vs.Root()
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net := newNet(&path)
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opt, err := nn.DefaultAdamConfig().Build(vs, LrNN)
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@ -60,28 +89,61 @@ func runCNN() {
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log.Fatal(err)
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}
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for epoch := 0; epoch < epochsCNN; epoch++ {
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var count = 0
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for {
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iter := ds.TrainIter(batchCNN).Shuffle()
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item, ok := iter.Next()
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if !ok {
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break
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}
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startTime := time.Now()
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loss := net.ForwardT(item.Data.MustTo(vs.Device(), true), true).CrossEntropyForLogits(item.Label.MustTo(vs.Device(), true))
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opt.BackwardStep(loss)
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loss.MustDrop()
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count++
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if count == 50 {
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for epoch := 0; epoch < epochsCNN; epoch++ {
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totalSize := ds.TrainImages.MustSize()[0]
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samples := int(totalSize)
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// index := ts.MustRandperm(int64(totalSize), gotch.Int64, gotch.CPU)
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// imagesTs := ds.TrainImages.MustIndexSelect(0, index, false)
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// labelsTs := ds.TrainLabels.MustIndexSelect(0, index, false)
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batches := samples / batchSize
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batchIndex := 0
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var epocLoss ts.Tensor
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// var loss ts.Tensor
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for i := 0; i < batches; i++ {
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start := batchIndex * batchSize
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size := batchSize
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if samples-start < batchSize {
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// size = samples - start
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break
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}
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fmt.Printf("completed \t %v batches\n", count)
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batchIndex += 1
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// Indexing
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narrowIndex := ts.NewNarrow(int64(start), int64(start+size))
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bImages := ds.TrainImages.Idx(narrowIndex)
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bLabels := ds.TrainLabels.Idx(narrowIndex)
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// bImages := imagesTs.Idx(narrowIndex)
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// bLabels := labelsTs.Idx(narrowIndex)
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bImages = bImages.MustTo(vs.Device(), true)
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bLabels = bLabels.MustTo(vs.Device(), true)
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logits := net.ForwardT(bImages, true)
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loss := logits.CrossEntropyForLogits(bLabels)
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opt.BackwardStep(loss)
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epocLoss = loss.MustShallowClone()
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epocLoss.Detach_()
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// fmt.Printf("completed \t %v batches\t %.2f\n", i, loss.Values()[0])
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bImages.MustDrop()
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bLabels.MustDrop()
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// logits.MustDrop()
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loss.MustDrop()
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}
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// testAccuracy := ts.BatchAccuracyForLogits(net, ds.TestImages, ds.TestLabels, vs.Device(), 1024)
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//
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// testAccuracy := ts.BatchAccuracyForLogits(net, testImages, testLabels, vs.Device(), 1024)
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// fmt.Printf("Epoch: %v \t Test accuracy: %.2f%%\n", epoch, testAccuracy*100)
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fmt.Printf("Epoch:\t %v\tLoss: \t %.2f\n", epoch, epocLoss.Values()[0])
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epocLoss.MustDrop()
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}
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fmt.Printf("Taken time:\t%.2f mins\n", time.Since(startTime).Minutes())
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}
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@ -41,7 +41,7 @@ func runLinear() {
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})
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testLogits := ds.TestImages.MustMm(ws, false).MustAdd(bs, true)
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testAccuracy := testLogits.MustArgmax(-1, false, true).MustEq1(ds.TestLabels, true).MustTotype(gotch.Float, true).MustMean(gotch.Float.CInt(), true).MustView([]int64{-1}).MustFloat64Value([]int64{0})
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testAccuracy := testLogits.MustArgmax(-1, false, true).MustEq1(ds.TestLabels, true).MustTotype(gotch.Float, true).MustMean(gotch.Float.CInt(), true).MustView([]int64{-1}, true).MustFloat64Value([]int64{0})
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fmt.Printf("Epoch: %v - Loss: %.3f - Test accuracy: %.2f%%\n", epoch, loss.Values()[0], testAccuracy*100)
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@ -72,7 +72,7 @@ func BatchAccuracyForLogits(m ModuleT, xs, ys Tensor, d gotch.Device, batchSize
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break
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}
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acc := m.ForwardT(item.Data.MustTo(d, true), false).AccuracyForLogits(item.Label.MustTo(d, true)).MustView([]int64{-1}).MustFloat64Value([]int64{0})
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acc := m.ForwardT(item.Data.MustTo(d, true), false).AccuracyForLogits(item.Label.MustTo(d, true)).MustView([]int64{-1}, false).MustFloat64Value([]int64{0})
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size := float64(item.Data.MustSize()[0])
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sumAccuracy += acc * size
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sampleCount += size
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@ -678,9 +678,11 @@ func (ts Tensor) MustMean(dtype int32, del bool) (retVal Tensor) {
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return retVal
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}
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func (ts Tensor) View(sizeData []int64) (retVal Tensor, err error) {
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func (ts Tensor) View(sizeData []int64, del bool) (retVal Tensor, err error) {
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ptr := (*lib.Ctensor)(unsafe.Pointer(C.malloc(0)))
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defer C.free(unsafe.Pointer(ptr))
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if del {
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defer ts.MustDrop()
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}
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lib.AtgView(ptr, ts.ctensor, sizeData, len(sizeData))
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if err = TorchErr(); err != nil {
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@ -692,8 +694,8 @@ func (ts Tensor) View(sizeData []int64) (retVal Tensor, err error) {
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return retVal, nil
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}
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func (ts Tensor) MustView(sizeData []int64) (retVal Tensor) {
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retVal, err := ts.View(sizeData)
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func (ts Tensor) MustView(sizeData []int64, del bool) (retVal Tensor) {
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retVal, err := ts.View(sizeData, del)
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if err != nil {
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log.Fatal(err)
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}
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@ -993,5 +993,5 @@ func (r Reduction) ToInt() (retVal int) {
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func (ts Tensor) Values() []float64 {
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clone := ts.MustShallowClone()
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clone.Detach_()
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return []float64{clone.MustView([]int64{-1}).MustFloat64Value([]int64{-1})}
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return []float64{clone.MustView([]int64{-1}, true).MustFloat64Value([]int64{-1})}
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}
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@ -125,7 +125,7 @@ func readImages(filename string) (retVal ts.Tensor) {
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err = fmt.Errorf("create images tensor err.")
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log.Fatal(err)
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}
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retVal = imagesTs.MustView([]int64{int64(samples), int64(rows * cols)}).MustTotype(gotch.Float, true).MustDiv1(ts.FloatScalar(255.0), true)
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retVal = imagesTs.MustView([]int64{int64(samples), int64(rows * cols)}, true).MustTotype(gotch.Float, true).MustDiv1(ts.FloatScalar(255.0), true)
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return retVal
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}
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