More work on tring to make torch work

logic/models/train/torch/modelloader/modelloader.go

@@ -35,6 +35,7 @@ func LoadImagesAndLables(db db.Db, m *types.BaseModel, mode types.DATA_POINT_MOD
 		if err != nil {
 			return
 		}
+
 		pimgs = append(pimgs, img)
 
 		t_label := make([]int, size)
@@ -46,6 +47,7 @@ func LoadImagesAndLables(db db.Db, m *types.BaseModel, mode types.DATA_POINT_MOD
 		if err != nil {
 			return
 		}
+
 		plabels = append(plabels, label)
 	}
 
@@ -62,13 +64,16 @@ func LoadImagesAndLables(db db.Db, m *types.BaseModel, mode types.DATA_POINT_MOD
 	count = len(pimgs)
 
 	imgs, err = torch.Stack(pimgs, 0)
+    if err != nil {
+        return
+    }
 
-	labels, err = labels.ToDtype(gotch.Float, false, false, true)
+	imgs, err = imgs.ToDtype(gotch.Float, false, false, true)
 	if err != nil {
 		return
 	}
 
-	imgs, err = imgs.ToDtype(gotch.Float, false, false, true)
+	labels, err = labels.ToDtype(gotch.Float, false, false, true)
 	if err != nil {
 		return
 	}
@@ -129,21 +134,46 @@ func (ds *Dataset) TestIter(batchSize int64) *torch.Iter2 {
 }
 
 func (ds *Dataset) TrainIter(batchSize int64) (iter *torch.Iter2, err error) {
-
+    
-    train_images, err := ds.TrainImages.DetachCopy(false)
+    // Create a clone of the trainimages
+    size, err := ds.TrainImages.Size()
     if err != nil {
         return
     }
 
-    train_labels, err := ds.TrainLabels.DetachCopy(false)
+    train_images, err := torch.Zeros(size, gotch.Float, ds.Device)
     if err != nil {
         return
     }
 
-    iter, err = torch.NewIter2(train_images, train_labels, batchSize)
+    ds.TrainImages, err = ds.TrainImages.Clone(train_images, false)
     if err != nil {
         return
     }
 
+
+
+    // Create a clone of the labels
+    size, err = ds.TrainLabels.Size()
+    if err != nil {
+        return
+    }
+
+    train_labels, err := torch.Zeros(size, gotch.Float, ds.Device)
+    if err != nil {
+        return
+    }
+
+    ds.TrainLabels, err = ds.TrainLabels.Clone(train_labels, false)
+    if err != nil {
+        return
+    }
+
+
+	iter, err = torch.NewIter2(train_images, train_labels, batchSize)
+	if err != nil {
+		return
+	}
+
 	return
 }

logic/models/train/torch/nn/linear.go

@@ -7,6 +7,7 @@ import (
 
 	"git.andr3h3nriqu3s.com/andr3/gotch/nn"
 	"git.andr3h3nriqu3s.com/andr3/gotch/ts"
+	"github.com/charmbracelet/log"
 )
 
 // LinearConfig is a configuration for a linear layer
@@ -104,6 +105,11 @@ func NewLinear(vs *Path, inDim, outDim int64, c *LinearConfig) *Linear {
 	}
 }
 
+func (l *Linear) Debug() {
+    log.Info("Ws", "ws", l.Ws.MustGrad(false).MustMax(false).Float64Values())
+    log.Info("Bs", "bs", l.Bs.MustGrad(false).MustMax(false).Float64Values())
+}
+
 func (l *Linear) ExtractFromVarstore(vs *VarStore) {
     l.Ws = vs.GetTensorOfVar(l.weight_name)
     l.Bs = vs.GetTensorOfVar(l.bias_name)

logic/models/train/torch/nn/utils.go

@@ -14,4 +14,5 @@ type MyLayer interface {
     torch.ModuleT
 
     ExtractFromVarstore(vs *VarStore)
+    Debug()
 }

logic/models/train/torch/torch.go

@@ -35,12 +35,12 @@ func (n *ContainerModel) ForwardT(x *torch.Tensor, train bool) *torch.Tensor {
 	for i := 0; i < len(n.Layers); i++ {
 		if i == 0 {
 			outs[0] = n.Layers[i].ForwardT(x, train)
-			defer outs[0].MustDrop()
+			//defer outs[0].MustDrop()
 		} else if i == len(n.Layers)-1 {
             return n.Layers[i].ForwardT(outs[i-1], train)
 		} else {
 			outs[i] = n.Layers[i].ForwardT(outs[i-1], train)
-			defer outs[i].MustDrop()
+		    //defer outs[i].MustDrop()
 		}
 	}
     panic("Do not reach here")
@@ -107,6 +107,12 @@ func BuildModel(layers []*types.Layer, _lastLinearSize int64, addSigmoid bool) *
 	return b
 }
 
+func (model *ContainerModel) Debug() {
+    for _, v := range model.Layers {
+        v.Debug()
+    }
+}
+
 func SaveModel(model *ContainerModel, modelFn string) (err error) {
 	model.Vs.ToDevice(gotch.CPU)
 	return model.Vs.Save(modelFn)

logic/models/train/torch/utils.go

@@ -107,6 +107,7 @@ func NewFlatten() *Flatten {
 
 // The flatten layer does not to move anything to the device
 func (b *Flatten) ExtractFromVarstore(vs *my_nn.VarStore) {}
+func (b *Flatten) Debug() {}
 
 // Forward method
 func (b *Flatten) Forward(x *torch.Tensor) *torch.Tensor {
@@ -133,6 +134,7 @@ func NewSigmoid() *Sigmoid {
 
 // The sigmoid layer does not need to move anything to another device
 func (b *Sigmoid) ExtractFromVarstore(vs *my_nn.VarStore) {}
+func (b *Sigmoid) Debug() {}
 
 func (b *Sigmoid) Forward(x *torch.Tensor) *torch.Tensor {
 	out, err := x.Sigmoid(false)

logic/models/train/train_normal.go

@@ -146,16 +146,6 @@ func trainDefinition(c BasePack, m *BaseModel, def *Definition, in_model *my_tor
 		return
 	}
 
-	/* opt1, err := my_nn.DefaultAdamConfig().Build(model.Vs, 0.001)
-		if err != nil {
-			return
-		}
-
-	    opt1.Debug() */
-
-	//log.Info("\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n")
-
-	// TODO remove this
 	model.To(device)
 	defer model.To(gotch.CPU)
 
@@ -192,7 +182,7 @@ func trainDefinition(c BasePack, m *BaseModel, def *Definition, in_model *my_tor
 			}
 
 			data := item.Data
-			data, err = data.ToDevice(device, gotch.Float, false, true, false)
+			data, err = data.ToDevice(device, gotch.Float, true, true, false)
 			if err != nil {
 				return
 			}
@@ -206,13 +196,39 @@ func trainDefinition(c BasePack, m *BaseModel, def *Definition, in_model *my_tor
 				return
 			}
 
-			pred := model.ForwardT(data, true)
+            var size []int64
+            size, err = data.Size()
+            if err != nil {
+                return
+            }
+
+            var ones *torch.Tensor
+            ones, err = torch.Ones(size, gotch.Float, device)
+            if err != nil {
+                return
+            }
+
+            ones, err = ones.SetRequiresGrad(true, true)
+            if err != nil {
+                return
+            }
+
+            err = ones.RetainGrad(false)
+            if err != nil {
+                return
+            }
+
+			//pred := model.ForwardT(data, true)
+			pred := model.ForwardT(ones, true)
 			pred, err = pred.SetRequiresGrad(true, true)
 			if err != nil {
 				return
 			}
 
-			pred.RetainGrad(false)
+			err = pred.RetainGrad(false)
+            if err != nil {
+                return
+            }
 
 			label := item.Label
 			label, err = label.ToDevice(device, gotch.Float, false, true, false)
@@ -223,10 +239,13 @@ func trainDefinition(c BasePack, m *BaseModel, def *Definition, in_model *my_tor
 			if err != nil {
 				return
 			}
-			label.RetainGrad(false)
+			err = label.RetainGrad(false)
+            if err != nil {
+                return
+            }
 
 			// Calculate loss
-			loss, err = pred.BinaryCrossEntropyWithLogits(label, &torch.Tensor{}, &torch.Tensor{}, 1, false)
+			loss, err = pred.BinaryCrossEntropyWithLogits(label, &torch.Tensor{}, &torch.Tensor{}, 2, false)
 			if err != nil {
 				return
 			}
@@ -234,6 +253,11 @@ func trainDefinition(c BasePack, m *BaseModel, def *Definition, in_model *my_tor
 			if err != nil {
 				return
 			}
+            err = loss.RetainGrad(false)
+            if err != nil {
+                return
+            }
+
 
 			err = opt.ZeroGrad()
 			if err != nil {
@@ -245,31 +269,24 @@ func trainDefinition(c BasePack, m *BaseModel, def *Definition, in_model *my_tor
 				return
 			}
 
-			err = opt.Step()
+
-			if err != nil {
+            log.Info("pred grad", "pred", pred.MustGrad(false).MustMax(false).Float64Values() )
-				return
+            log.Info("pred grad", "ones", ones.MustGrad(false).MustMax(false).Float64Values(), "lol", ones.MustRetainsGrad(false) )
-			}
+            log.Info("pred grad", "data", data.MustGrad(false).MustMax(false).Float64Values(), "lol", data.MustRetainsGrad(false) )
+            log.Info("pred grad", "outs", label.MustGrad(false).MustMax(false).Float64Values() )
 
 			vars := model.Vs.Variables()
 
 			for k, v := range vars {
-				var grad *torch.Tensor
+                log.Info("[grad check]", "k", k, "grad", v.MustGrad(false).MustMax(false).Float64Values(), "lol", v.MustRetainsGrad(false) )
-				grad, err = v.Grad(false)
+			}
-				if err != nil {
-					return
-				}
 
-				grad, err = grad.Abs(false)
+            model.Debug()
-				if err != nil {
-					return
-				}
 
-				grad, err = grad.Max(false)
-				if err != nil {
-					return
-				}
 
-				log.Info("[grad check]", "k", k, "grad", grad.Float64Values())
+			err = opt.Step()
+			if err != nil {
+				return
 			}
 
 			trainLoss = loss.Float64Values()[0]
@@ -295,7 +312,7 @@ func trainDefinition(c BasePack, m *BaseModel, def *Definition, in_model *my_tor
 				}
 			} */
 
-			// panic("fornow")
+			panic("fornow")
 		}
 
 		//v := []float64{}

test.go

@@ -11,7 +11,7 @@ import (
 	"github.com/charmbracelet/log"
 )
 
-func main_() {
+func _main() {
 
 	log.Info("Hello world")
 
@@ -27,6 +27,10 @@ func main_() {
 			LayerType: dbtypes.LAYER_DENSE,
 			Shape:     "[ 10 ]",
 		},
+		&dbtypes.Layer{
+			LayerType: dbtypes.LAYER_DENSE,
+			Shape:     "[ 10 ]",
+		},
 	}, 0, true)
 
     var err error