fix: index tensor on cpu (#961)

### Summary of Changes

The index tensor of an `ImageDataset` sometimes ended up on the CPU,
instead of the default device, which led to runtime errors. This PR
fixes this.

docs/tutorials/convolutional_neural_network_for_image_classification.ipynb

@@ -47,7 +47,6 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "id": "initial_id",
    "metadata": {
     "collapsed": true
@@ -57,7 +56,8 @@
     "\n",
     "images, filepaths = ImageList.from_files(\"data/shapes\", return_filenames=True)"
    ],
-   "outputs": []
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -84,8 +84,8 @@
     "collapsed": false
    },
    "id": "66dcf95a3fa51f23",
-   "execution_count": null,
-   "outputs": []
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -108,8 +108,8 @@
     "collapsed": false
    },
    "id": "32056ddf5396e070",
-   "execution_count": null,
-   "outputs": []
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -149,8 +149,8 @@
     "collapsed": false
    },
    "id": "806a8091249d533a",
-   "execution_count": null,
-   "outputs": []
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -175,8 +175,8 @@
     "collapsed": false
    },
    "id": "af68cc0d32655d32",
-   "execution_count": null,
-   "outputs": []
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -198,15 +198,13 @@
   },
   {
    "cell_type": "code",
-   "source": [
-    "cnn_fitted = cnn.fit(dataset, epoch_size=32, batch_size=16)"
-   ],
+   "source": "cnn_fitted = cnn.fit(dataset, epoch_size=8, batch_size=16)",
    "metadata": {
     "collapsed": false
    },
    "id": "381627a94d500675",
-   "execution_count": null,
-   "outputs": []
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -227,8 +225,8 @@
     "collapsed": false
    },
    "id": "62f63dd68362c8b7",
-   "execution_count": null,
-   "outputs": []
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -249,8 +247,8 @@
     "collapsed": false
    },
    "id": "779277d73e30554d",
-   "execution_count": null,
-   "outputs": []
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -271,8 +269,8 @@
     "collapsed": false
    },
    "id": "a5ddbbfba41aa7f",
-   "execution_count": null,
-   "outputs": []
+   "outputs": [],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -293,8 +291,8 @@
     "collapsed": false
    },
    "id": "7081595d7100fb42",
-   "execution_count": null,
-   "outputs": []
+   "outputs": [],
+   "execution_count": null
   }
  ],
  "metadata": {

src/safeds/data/labeled/containers/_image_dataset.py

@@ -356,6 +356,8 @@ def split(
             upper_bound=_ClosedBound(1),
         )
 
+        _init_default_device()
+
         first_dataset: ImageDataset[Out_co] = copy.copy(self)
         second_dataset: ImageDataset[Out_co] = copy.copy(self)
 

src/safeds/ml/nn/converters/_input_converter_image_to_column.py

@@ -6,14 +6,14 @@
 from safeds.data.image.containers._single_size_image_list import _SingleSizeImageList
 from safeds.data.labeled.containers import ImageDataset
 from safeds.data.labeled.containers._image_dataset import _ColumnAsTensor
-from safeds.data.tabular.containers import Column
 
 from ._input_converter_image import _InputConversionImage
 
 if TYPE_CHECKING:
     from torch import Tensor
 
     from safeds.data.image.containers import ImageList
+    from safeds.data.tabular.containers import Column
     from safeds.data.tabular.transformation import OneHotEncoder
 
 
@@ -43,9 +43,9 @@ def _data_conversion_output(
         output = torch.zeros(len(input_data), len(one_hot_encoder._get_names_of_added_columns()))
         output[torch.arange(len(input_data)), output_data] = 1
 
-        im_dataset: ImageDataset[Column] = ImageDataset[Column].__new__(ImageDataset)
+        im_dataset: ImageDataset[Column] = object.__new__(ImageDataset)
         im_dataset._output = _ColumnAsTensor._from_tensor(output, column_name, one_hot_encoder)
-        im_dataset._shuffle_tensor_indices = torch.LongTensor(list(range(len(input_data))))
+        im_dataset._shuffle_tensor_indices = torch.arange(len(input_data))
         im_dataset._shuffle_after_epoch = False
         im_dataset._batch_size = 1
         im_dataset._next_batch_index = 0

src/safeds/ml/nn/converters/_input_converter_image_to_table.py

@@ -6,14 +6,14 @@
 from safeds.data.image.containers._single_size_image_list import _SingleSizeImageList
 from safeds.data.labeled.containers import ImageDataset
 from safeds.data.labeled.containers._image_dataset import _TableAsTensor
-from safeds.data.tabular.containers import Table
 
 from ._input_converter_image import _InputConversionImage
 
 if TYPE_CHECKING:
     from torch import Tensor
 
     from safeds.data.image.containers import ImageList
+    from safeds.data.tabular.containers import Table
 
 
 class InputConversionImageToTable(_InputConversionImage):
@@ -33,9 +33,9 @@ def _data_conversion_output(self, input_data: ImageList, output_data: Tensor) ->
         output = torch.zeros(len(input_data), len(column_names))
         output[torch.arange(len(input_data)), output_data] = 1
 
-        im_dataset: ImageDataset[Table] = ImageDataset[Table].__new__(ImageDataset)
+        im_dataset: ImageDataset[Table] = object.__new__(ImageDataset)
         im_dataset._output = _TableAsTensor._from_tensor(output, column_names)
-        im_dataset._shuffle_tensor_indices = torch.LongTensor(list(range(len(input_data))))
+        im_dataset._shuffle_tensor_indices = torch.arange(len(input_data))
         im_dataset._shuffle_after_epoch = False
         im_dataset._batch_size = 1
         im_dataset._next_batch_index = 0