Fix tf.decode_jpeg and SetOpAttrScalar for float.

Author: Oceania2018

src/TensorFlowNET.Core/APIs/tf.image.cs

@@ -16,6 +16,7 @@ limitations under the License.
 
 using System.Collections.Generic;
 using Tensorflow.IO;
+using static Tensorflow.Binding;
 
 namespace Tensorflow
 {
@@ -59,6 +60,10 @@ public Tensor resize_images(Tensor images, Tensor size, string method = ResizeMe
                 string name = null)
                 => image_ops_impl.resize_images(images, size, method, preserve_aspect_ratio, antialias, name);
 
+            public Tensor resize_images_v2(Tensor images, TensorShape size, string method = ResizeMethod.BILINEAR, bool preserve_aspect_ratio = false, bool antialias = false,
+                string name = null)
+                => image_ops_impl.resize_images(images, tf.constant(size.dims), method, preserve_aspect_ratio, antialias, name);
+
             public Tensor resize_images_with_pad(Tensor image, int target_height, int target_width, string method, bool antialias)
                 => image_ops_impl.resize_images_with_pad(image, target_height, target_width, method, antialias);
 
@@ -160,7 +165,7 @@ public Tensor decode_jpeg(Tensor contents,
                         int ratio = 1,
                         bool fancy_upscaling = true,
                         bool try_recover_truncated = false,
-                        float acceptable_fraction = 1,
+                        int acceptable_fraction = 1,
                         string dct_method = "",
                         string name = null)
                 => gen_image_ops.decode_jpeg(contents, channels: channels, ratio: ratio,

src/TensorFlowNET.Core/Eager/EagerRunner.TFE_FastPathExecute.cs

@@ -376,6 +376,9 @@ bool SetOpAttrScalar(Context ctx, SafeOpHandle op,
                 case TF_AttrType.TF_ATTR_INT:
                     c_api.TFE_OpSetAttrInt(op, key, Convert.ToInt64(value));
                     break;
+                case TF_AttrType.TF_ATTR_FLOAT:
+                    c_api.TFE_OpSetAttrFloat(op, key, Convert.ToSingle(value));
+                    break;
                 case TF_AttrType.TF_ATTR_SHAPE:
                     var dims = (value as int[]).Select(x => (long)x).ToArray();
                     c_api.TFE_OpSetAttrShape(op, key, dims, dims.Length, status.Handle);

src/TensorFlowNET.Core/Eager/c_api.eager.cs

@@ -176,6 +176,9 @@ public static void TFE_Execute(SafeOpHandle op, SafeTensorHandleHandle[] retvals
         [DllImport(TensorFlowLibName)]
         public static extern void TFE_OpSetAttrInt(SafeOpHandle op, string attr_name, long value);
 
+        [DllImport(TensorFlowLibName)]
+        public static extern void TFE_OpSetAttrFloat(SafeOpHandle op, string attr_name, float value);
+
         /// <summary>
         /// 
         /// </summary>

src/TensorFlowNET.Core/Keras/Preprocessings/DatasetUtils.get_training_or_validation_split.cs

@@ -0,0 +1,43 @@
+using System;
+using System.Collections.Generic;
+using System.IO;
+using System.Linq;
+using System.Text;
+
+namespace Tensorflow.Keras.Preprocessings
+{
+    public partial class DatasetUtils
+    {
+        /// <summary>
+        /// Potentially restict samples & labels to a training or validation split.
+        /// </summary>
+        /// <param name="samples"></param>
+        /// <param name="labels"></param>
+        /// <param name="validation_split"></param>
+        /// <param name="subset"></param>
+        /// <returns></returns>
+        public (T1[], T2[]) get_training_or_validation_split<T1, T2>(T1[] samples,
+            T2[] labels, 
+            float validation_split, 
+            string subset)
+        {
+            var num_val_samples = Convert.ToInt32(samples.Length * validation_split);
+            if (subset == "training")
+            {
+                Console.WriteLine($"Using {samples.Length - num_val_samples} files for training.");
+                samples = samples[..^num_val_samples];
+                labels = labels[..^num_val_samples];
+            }
+            else if (subset == "validation")
+            {
+                Console.WriteLine($"Using {num_val_samples} files for validation.");
+                samples = samples[samples.Length..];
+                labels = labels[samples.Length..];
+            }
+            else
+                throw new NotImplementedException("");
+
+            return (samples, labels);
+        }
+    }
+}

src/TensorFlowNET.Core/Keras/Preprocessings/DatasetUtils.index_directory.cs

@@ -1,5 +1,8 @@
-using System;
+using NumSharp;
+using System;
 using System.Collections.Generic;
+using System.IO;
+using System.Linq;
 using System.Text;
 
 namespace Tensorflow.Keras.Preprocessings
@@ -20,14 +23,45 @@ public partial class DatasetUtils
         /// file_paths, labels, class_names
         /// </returns>
         public (string[], int[], string[]) index_directory(string directory,
-            string labels,
-            string[] formats,
-            string class_names = null,
+            string[] formats = null,
+            string[] class_names = null,
             bool shuffle = true,
             int? seed = null,
             bool follow_links = false)
         {
-            throw new NotImplementedException("");
+            var labels = new List<int>();
+            var file_paths = new List<string>();
+
+            var class_dirs = Directory.GetDirectories(directory);
+            class_names = class_dirs.Select(x => x.Split(Path.DirectorySeparatorChar)[^1]).ToArray();
+
+            for (var label = 0; label < class_dirs.Length; label++)
+            {
+                var files = Directory.GetFiles(class_dirs[label]);
+                file_paths.AddRange(files);
+                labels.AddRange(Enumerable.Range(0, files.Length).Select(x => label));
+            }
+
+            var return_labels = new int[labels.Count];
+            var return_file_paths = new string[file_paths.Count];
+
+            if (shuffle)
+            {
+                if (!seed.HasValue)
+                    seed = np.random.randint((long)1e6);
+                var random_index = np.arange(labels.Count);
+                var rng = np.random.RandomState(seed.Value);
+                rng.shuffle(random_index);
+                var index = random_index.ToArray<int>();
+
+                for (int i = 0; i< labels.Count; i++)
+                {
+                    return_labels[i] = labels[index[i]];
+                    return_file_paths[i] = file_paths[index[i]];
+                }
+            }
+
+            return (return_file_paths, return_labels, class_names);
         }
     }
 }

src/TensorFlowNET.Core/Keras/Preprocessings/Preprocessing.image_dataset_from_directory.cs

@@ -28,7 +28,7 @@ public partial class Preprocessing
         public Tensor image_dataset_from_directory(string directory,
             string labels = "inferred",
             string label_mode = "int",
-            string class_names = null,
+            string[] class_names = null,
             string color_mode = "rgb",
             int batch_size = 32,
             TensorShape image_size = null,
@@ -44,13 +44,15 @@ public Tensor image_dataset_from_directory(string directory,
                 num_channels = 3;
             // C:/Users/haipi/.keras/datasets/flower_photos
             var (image_paths, label_list, class_name_list) = tf.keras.preprocessing.dataset_utils.index_directory(directory,
-                labels,
-                WHITELIST_FORMATS,
+                formats: WHITELIST_FORMATS,
                 class_names: class_names,
                 shuffle: shuffle,
                 seed: seed,
                 follow_links: follow_links);
 
+            (image_paths, label_list) = tf.keras.preprocessing.dataset_utils.get_training_or_validation_split(image_paths, label_list, validation_split, subset);
+
+            paths_and_labels_to_dataset(image_paths, image_size, num_channels, label_list, label_mode, class_name_list.Length, interpolation);
             throw new NotImplementedException("");
         }
     }

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

@@ -0,0 +1,32 @@
+using System;
+using System.Globalization;
+using static Tensorflow.Binding;
+
+namespace Tensorflow.Keras
+{
+    public partial class Preprocessing
+    {
+        public Tensor paths_and_labels_to_dataset(string[] image_paths,
+            TensorShape image_size,
+            int num_channels,
+            int[] labels,
+            string label_mode,
+            int num_classes,
+            string interpolation)
+        {
+            foreach (var image_path in image_paths)
+                path_to_image(image_path, image_size, num_channels, interpolation);
+
+            throw new NotImplementedException("");
+        }
+
+        Tensor path_to_image(string path, TensorShape image_size, int num_channels, string interpolation)
+        {
+            var img = tf.io.read_file(path);
+            img = tf.image.decode_image(
+                img, channels: num_channels, expand_animations: false);
+            img = tf.image.resize_images_v2(img, image_size, method: interpolation);
+            return img;
+        }
+    }
+}

src/TensorFlowNET.Core/Operations/control_flow_ops.cs

@@ -86,7 +86,7 @@ public static Operation Assert(Tensor condition, object[] data, int? summarize =
 
                 var guarded_assert = cond(condition, false_assert, true_assert, name: "AssertGuard");
 
-                return guarded_assert[0].op;
+                return guarded_assert == null ? null : guarded_assert[0].op;
             });
         }
 
@@ -423,8 +423,6 @@ public static Tensor cond(Tensor pred,
                         return true_fn() as Tensor;
                     else
                         return false_fn() as Tensor;
-
-                    return null;
                 }
 
                 // Add the Switch to the graph.
@@ -507,8 +505,6 @@ public static Tensor[] cond<T>(Tensor pred,
                         return true_fn() as Tensor[];
                     else
                         return false_fn() as Tensor[];
-
-                    return null;
                 }
 
                 // Add the Switch to the graph.

src/TensorFlowNET.Core/Operations/gen_image_ops.cs

@@ -66,14 +66,24 @@ public static Tensor decode_jpeg(Tensor contents,
             int ratio = 1,
             bool fancy_upscaling = true,
             bool try_recover_truncated = false,
-            float acceptable_fraction = 1,
+            int acceptable_fraction = 1,
             string dct_method = "",
             string name = null)
         {
             // Add nodes to the TensorFlow graph.
             if (tf.Context.executing_eagerly())
             {
-                throw new NotImplementedException("decode_jpeg");
+                var results = tf.Runner.TFE_FastPathExecute(tf.Context, tf.Context.DeviceName,
+                    "DecodeJpeg", name,
+                    null,
+                    contents,
+                    "channels", channels,
+                    "ratio", ratio,
+                    "fancy_upscaling", fancy_upscaling,
+                    "try_recover_truncated", try_recover_truncated, 
+                    "acceptable_fraction", acceptable_fraction,
+                    "dct_method", dct_method);
+                return results[0];
             }
             else
             {
@@ -171,17 +181,42 @@ public static Tensor resize_bilinear(Tensor images,
                     "half_pixel_centers", half_pixel_centers);
                 return results[0];
             }
-            else
+
+            var _op = tf.OpDefLib._apply_op_helper("ResizeBilinear", name: name, args: new
             {
-                var _op = tf.OpDefLib._apply_op_helper("ResizeBilinear", name: name, args: new
-                {
-                    images,
-                    size,
-                    align_corners
-                });
+                images,
+                size,
+                align_corners
+            });
 
-                return _op.outputs[0];
+            return _op.outputs[0];
+        }
+
+        public static Tensor resize_bicubic(Tensor images,
+            Tensor size,
+            bool align_corners = false,
+            bool half_pixel_centers = false,
+            string name = null)
+        {
+            if (tf.Context.executing_eagerly())
+            {
+                var results = tf.Runner.TFE_FastPathExecute(tf.Context, tf.Context.DeviceName,
+                    "ResizeBicubic", name,
+                    null,
+                    images, size,
+                    "align_corners", align_corners,
+                    "half_pixel_centers", half_pixel_centers);
+                return results[0];
             }
+
+            var _op = tf.OpDefLib._apply_op_helper("ResizeBicubic", name: name, args: new
+            {
+                images,
+                size,
+                align_corners
+            });
+
+            return _op.outputs[0];
         }
 
         public static Tensor resize_nearest_neighbor<Tsize>(Tensor images, Tsize size, bool align_corners = false, 

src/TensorFlowNET.Core/Operations/gen_ops.cs

@@ -24715,13 +24715,12 @@ public static Tensor resize_area (Tensor images, Tensor size, bool? align_corner
         /// <remarks>
         ///    Input images can be of different types but output images are always float.
         /// </remarks>
-        public static Tensor resize_bicubic (Tensor images, Tensor size, bool? align_corners = null, string name = "ResizeBicubic")
+        public static Tensor resize_bicubic (Tensor images, Tensor size, bool align_corners = false, bool half_pixel_centers = false, string name = "ResizeBicubic")
         {
             var dict = new Dictionary<string, object>();
             dict["images"] = images;
             dict["size"] = size;
-            if (align_corners.HasValue)
-                dict["align_corners"] = align_corners.Value;
+            dict["align_corners"] = align_corners;
             var op = tf.OpDefLib._apply_op_helper("ResizeBicubic", name: name, keywords: dict);
             return op.output;
         }