BREAKING CHANGE(nn/conv): removed pointer receiver at NewConv2D

example/mnist/cnn.go

@@ -28,11 +28,11 @@ type Net struct {
 	fc2   nn.Linear
 }
 
-func newNet(vs *nn.Path) Net {
+func newNet(vs nn.Path) Net {
 	conv1 := nn.NewConv2D(vs, 1, 32, 5, nn.DefaultConv2DConfig())
 	conv2 := nn.NewConv2D(vs, 32, 64, 5, nn.DefaultConv2DConfig())
-	fc1 := nn.NewLinear(*vs, 1024, 1024, nn.DefaultLinearConfig())
-	fc2 := nn.NewLinear(*vs, 1024, 10, nn.DefaultLinearConfig())
+	fc1 := nn.NewLinear(vs, 1024, 1024, nn.DefaultLinearConfig())
+	fc2 := nn.NewLinear(vs, 1024, 10, nn.DefaultLinearConfig())
 
 	return Net{
 		conv1,
@@ -83,8 +83,7 @@ func runCNN1() {
 	cuda := gotch.CudaBuilder(0)
 	vs := nn.NewVarStore(cuda.CudaIfAvailable())
 	// vs := nn.NewVarStore(gotch.CPU)
-	path := vs.Root()
-	net := newNet(&path)
+	net := newNet(vs.Root())
 	opt, err := nn.DefaultAdamConfig().Build(vs, LrCNN)
 	if err != nil {
 		log.Fatal(err)
@@ -161,8 +160,7 @@ func runCNN2() {
 
 	cuda := gotch.CudaBuilder(0)
 	vs := nn.NewVarStore(cuda.CudaIfAvailable())
-	path := vs.Root()
-	net := newNet(&path)
+	net := newNet(vs.Root())
 	opt, err := nn.DefaultAdamConfig().Build(vs, LrNN)
 	if err != nil {
 		log.Fatal(err)

example/transfer-learning/main.go

@@ -54,7 +54,7 @@ func main() {
 
 	// Pre-compute the final activations.
 
-	linear := nn.NewLinear(vs.Root(), 512, dataset.Labels, *nn.DefaultLinearConfig())
+	linear := nn.NewLinear(vs.Root(), 512, dataset.Labels, nn.DefaultLinearConfig())
 	sgd, err := nn.DefaultSGDConfig().Build(vs, 1e-3)
 	if err != nil {
 		log.Fatal(err)

nn/conv.go

@@ -90,7 +90,7 @@ type Conv2D struct {
 	Config Conv2DConfig
 }
 
-func NewConv2D(vs *Path, inDim, outDim int64, k int64, cfg Conv2DConfig) Conv2D {
+func NewConv2D(vs Path, inDim, outDim int64, k int64, cfg Conv2DConfig) Conv2D {
 	var conv Conv2D
 	conv.Config = cfg
 	if cfg.Bias {
@@ -179,7 +179,7 @@ func NewConv(vs Path, inDim, outDim int64, ksizes []int64, config interface{}) C
 	case len(ksizes) == 1 && configVal.Type() == reflect.TypeOf(Conv1DConfig{}):
 		return NewConv1D(&vs, inDim, outDim, ksizes[0], config.(Conv1DConfig))
 	case len(ksizes) == 2 && configVal.Type() == reflect.TypeOf(Conv2DConfig{}):
-		return NewConv2D(&vs, inDim, outDim, ksizes[0], config.(Conv2DConfig))
+		return NewConv2D(vs, inDim, outDim, ksizes[0], config.(Conv2DConfig))
 	case len(ksizes) == 3 && configVal.Type() == reflect.TypeOf(Conv3DConfig{}):
 		return NewConv3D(&vs, inDim, outDim, ksizes[0], config.(Conv3DConfig))
 

vision/alexnet.go

@@ -13,7 +13,7 @@ func anConv2d(p nn.Path, cIn, cOut, ksize, padding, stride int64) (retVal nn.Con
 	config.Stride = []int64{stride, stride}
 	config.Padding = []int64{padding, padding}
 
-	return nn.NewConv2D(&p, cIn, cOut, ksize, config)
+	return nn.NewConv2D(p, cIn, cOut, ksize, config)
 }
 
 func anMaxPool2d(xs ts.Tensor, ksize, stride int64) (retVal ts.Tensor) {

vision/densenet.go

@@ -18,7 +18,7 @@ func dnConv2d(p nn.Path, cIn, cOut, ksize, padding, stride int64) (retVal nn.Con
 	config.Padding = []int64{padding, padding}
 	config.Bias = false
 
-	return nn.NewConv2D(&p, cIn, cOut, ksize, config)
+	return nn.NewConv2D(p, cIn, cOut, ksize, config)
 }
 
 func denseLayer(p nn.Path, cIn, bnSize, growth int64) (retVal ts.ModuleT) {

vision/efficientnet.go

@@ -75,7 +75,7 @@ func (p params) roundFilters(filters int64) (retVal int64) {
 
 // Conv2D with same padding
 func enConv2d(vs nn.Path, i, o, k int64, c nn.Conv2DConfig, train bool) (retVal ts.ModuleT) {
-	conv2d := nn.NewConv2D(&vs, i, o, k, c)
+	conv2d := nn.NewConv2D(vs, i, o, k, c)
 	s := c.Stride
 
 	return nn.NewFunc(func(xs ts.Tensor) (res ts.Tensor) {

vision/inception.go

@@ -20,7 +20,7 @@ func convBn(p nn.Path, cIn, cOut, ksize, pad, stride int64) (retVal ts.ModuleT)
 	seq := nn.SeqT()
 
 	convP := p.Sub("conv")
-	seq.Add(nn.NewConv2D(&convP, cIn, cOut, ksize, convConfig))
+	seq.Add(nn.NewConv2D(convP, cIn, cOut, ksize, convConfig))
 
 	seq.Add(nn.BatchNorm2D(p.Sub("bn"), cOut, bnConfig))
 

vision/mobilenet.go

@@ -0,0 +1,24 @@
+package vision
+
+// MobileNet V2 implementation.
+// https://ai.googleblog.com/2018/04/mobilenetv2-next-generation-of-on.html
+
+import (
+	"github.com/sugarme/gotch/nn"
+	ts "github.com/sugarme/gotch/tensor"
+)
+
+// Conv2D + BatchNorm2D + ReLU6
+func cbr(p nn.Path, cIn, cOut, ks, stride, g int64) (retVal ts.ModuleT) {
+	config := nn.DefaultConv2DConfig()
+	config.Stride = []int64{stride, stride}
+	pad := (ks - 1) / 2
+	config.Padding = []int64{pad, pad}
+	config.Groups = g
+	config.Bias = false
+
+	seq := nn.SeqT()
+	seq.Add(nn.NewConv2D(p.Sub("0"), cIn, cOut, ks, config))
+
+	return seq
+}

vision/resnet.go

@@ -18,7 +18,7 @@ func conv2d(path nn.Path, cIn, cOut, ksize, padding, stride int64) (retVal nn.Co
 	config.Padding = []int64{padding, padding}
 	config.Bias = false
 
-	return nn.NewConv2D(&path, cIn, cOut, ksize, config)
+	return nn.NewConv2D(path, cIn, cOut, ksize, config)
 }
 
 func downSample(path nn.Path, cIn, cOut, stride int64) (retVal ts.ModuleT) {

vision/vgg.go

@@ -57,7 +57,7 @@ func vggConv2d(path nn.Path, cIn, cOut int64) (retVal nn.Conv2D) {
 	config.Stride = []int64{1, 1}
 	config.Padding = []int64{1, 1}
 
-	return nn.NewConv2D(&path, cIn, cOut, 3, config)
+	return nn.NewConv2D(path, cIn, cOut, 3, config)
 }
 
 func vgg(path nn.Path, config [][]int64, nclasses int64, batchNorm bool) nn.SequentialT {