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
 }