load all images for paths_and_labels_to_dataset

src/TensorFlowNET.Core/Tensors/Tensor.Creation.cs

@@ -42,7 +42,7 @@ namespace Tensorflow
         public unsafe Tensor(SafeTensorHandle handle, bool clone = false)
         {
             _handle = handle;
-            if (clone)
+            if (clone && handle != null)
                 _handle = TF_NewTensor(shape, dtype, data: TensorDataPointer.ToPointer());
                 
             isCreatedInGraphMode = !tf.executing_eagerly();

src/TensorFlowNET.Core/Tensors/dtypes.cs

@@ -17,6 +17,7 @@
 using Tensorflow.NumPy;
 using System;
 using System.Numerics;
+using System.Diagnostics;
 
 namespace Tensorflow
 {
@@ -221,6 +222,7 @@ namespace Tensorflow
             return (int)type > 100 ? (DataType)((int)type - 100) : type;
         }
 
+        [DebuggerStepThrough]
         public static TF_DataType as_tf_dtype(this DataType type)
         {
             return (TF_DataType)type;

src/TensorFlowNET.Core/Variables/BaseResourceVariable.cs

@@ -222,7 +222,7 @@ namespace Tensorflow
         public override string ToString()
         {
             if (tf.Context.executing_eagerly())
-                return $"tf.Variable: '{Name}' shape={string.Join(",", shape)}, dtype={dtype.as_numpy_name()}, numpy={read_value()}";
+                return $"tf.Variable: '{Name}' shape={string.Join(",", shape)}, dtype={dtype.as_numpy_name()}, numpy={read_value().numpy()}";
             else
                 return $"tf.Variable: '{Name}' shape={string.Join(",", shape)}, dtype={dtype.as_numpy_name()}";
         }

src/TensorFlowNET.Keras/Preprocessings/Preprocessing.paths_and_labels_to_dataset.cs

@@ -1,6 +1,7 @@
 using System;
 using System.IO;
 using static Tensorflow.Binding;
+using Tensorflow.NumPy;
 
 namespace Tensorflow.Keras
 {
@@ -14,9 +15,23 @@ namespace Tensorflow.Keras
             int num_classes,
             string interpolation)
         {
-            var path_ds = tf.data.Dataset.from_tensor_slices(image_paths);
-            var img_ds = path_ds.map(x => path_to_image(x, image_size, num_channels, interpolation));
+            // option 1: will load all images into memory, not efficient
+            var images = np.zeros((image_paths.Length, image_size[0], image_size[1], num_channels), np.float32);
+            for (int i = 0; i < len(images); i++)
+            {
+                var img = tf.io.read_file(image_paths[i]);
+                img = tf.image.decode_image(
+                    img, channels: num_channels, expand_animations: false);
+                var resized_image = tf.image.resize_images_v2(img, image_size, method: interpolation);
+                images[i] = resized_image.numpy();
+                tf_output_redirect.WriteLine(image_paths[i]);
+            };
+
+            // option 2: dynamic load, but has error, need to fix
+            /* var path_ds = tf.data.Dataset.from_tensor_slices(image_paths);
+            var img_ds = path_ds.map(x => path_to_image(x, image_size, num_channels, interpolation));*/
 
+            var img_ds = tf.data.Dataset.from_tensor_slices(images);
             if (label_mode == "int")
             {
                 var label_ds = dataset_utils.labels_to_dataset(labels, label_mode, num_classes);