Add ParallelMapDataset

src/TensorFlowNET.Core/Data/DatasetOptions.cs

@@ -0,0 +1,10 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow
+{
+    public class DatasetOptions
+    {
+    }
+}

src/TensorFlowNET.Core/Data/DatasetV2.cs

@@ -60,12 +60,18 @@ namespace Tensorflow
                 preserve_cardinality: preserve_cardinality,
                 use_legacy_function: use_legacy_function);
 
+        public IDatasetV2 map(Func<Tensor, (Tensor, Tensor), (Tensor, Tensor)> map_func, int num_parallel_calls = -1)
+            => new ParallelMapDataset(this, map_func, num_parallel_calls: num_parallel_calls);
+
         public IDatasetV2 flat_map(Func<Tensor, IDatasetV2> map_func)
             => new FlatMapDataset(this, map_func);
 
         public IDatasetV2 model(AutotuneAlgorithm algorithm, long cpu_budget)
             => new ModelDataset(this, algorithm, cpu_budget);
 
+        public IDatasetV2 with_options(DatasetOptions options)
+            => new OptionsDataset(this, options);
+
         public IDatasetV2 apply_options()
         {
             // (1) Apply threading options
@@ -94,7 +100,7 @@ namespace Tensorflow
         }
 
         public override string ToString()
-            => $"{GetType().Name} shapes: ({structure[0].shape}, {structure[1].shape}), types: (tf.{structure[0].dtype.as_numpy_name()}, tf.{structure[1].dtype.as_numpy_name()})";
+            => $"{GetType().Name} shapes: {string.Join(", ", structure.Select(x => x.shape))}, types: {string.Join(", ", structure.Select(x => "tf." + x.dtype.as_numpy_name()))}";
 
         public IEnumerator<(Tensor, Tensor)> GetEnumerator()
         {

src/TensorFlowNET.Core/Data/FlatMapDataset.cs

@@ -1,5 +1,6 @@
 using System;
 using System.Collections.Generic;
+using System.Linq;
 using System.Text;
 using Tensorflow.Functions;
 
@@ -14,7 +15,7 @@ namespace Tensorflow
             Func<Tensor, IDatasetV2> map_func) : base(input_dataset)
         {
             var func = new ConcreteFunction(map_func, input_dataset.element_spec[0].dtype);
-
+            structure = func.OutputStructure;
             variant_tensor = ops.flat_map_dataset(input_dataset.variant_tensor,
                 func,
                 output_types,

src/TensorFlowNET.Core/Data/IDatasetV2.cs

@@ -62,10 +62,15 @@ namespace Tensorflow
             bool preserve_cardinality = false,
             bool use_legacy_function = false);
 
+        IDatasetV2 map(Func<Tensor, (Tensor, Tensor), (Tensor, Tensor)> map_func, 
+            int num_parallel_calls = -1);
+
         IDatasetV2 flat_map(Func<Tensor, IDatasetV2> map_func);
 
         IDatasetV2 model(AutotuneAlgorithm algorithm, long cpu_budget);
 
+        IDatasetV2 with_options(DatasetOptions options);
+
         /// <summary>
         /// Apply options, such as optimization configuration, to the dataset.
         /// </summary>

src/TensorFlowNET.Core/Data/OptionsDataset.cs

@@ -0,0 +1,21 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow
+{
+    /// <summary>
+    /// An identity `Dataset` that stores options.
+    /// </summary>
+    public class OptionsDataset : UnaryUnchangedStructureDataset
+    {
+        DatasetOptions options;
+
+        public OptionsDataset(IDatasetV2 input_dataset, DatasetOptions options) 
+            : base(input_dataset)
+        {
+            this.options = options;
+            variant_tensor = input_dataset.variant_tensor;
+        }
+    }
+}

src/TensorFlowNET.Core/Data/ParallelMapDataset.cs

@@ -0,0 +1,34 @@
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using System.Text;
+using Tensorflow.Functions;
+using static Tensorflow.Binding;
+
+namespace Tensorflow
+{
+    //A `Dataset` that maps a function over elements in its input in parallel.
+    public class ParallelMapDataset : UnaryDataset
+    {
+        public ParallelMapDataset(IDatasetV2 input_dataset, 
+            Func<Tensor, (Tensor, Tensor), (Tensor, Tensor)> map_func,
+            int num_parallel_calls = -1,
+            bool use_inter_op_parallelism = true,
+            bool preserve_cardinality = false,
+            bool use_legacy_function = false) : base(input_dataset)
+        {
+            var func = new ConcreteFunction(map_func, 
+                input_dataset.element_spec.Select(x => x.dtype).ToArray(),
+                input_dataset.element_spec.Select(x => x.shape).ToArray());
+
+            structure = func.OutputStructure;
+            var _num_parallel_calls = tf.convert_to_tensor(num_parallel_calls, dtype: tf.int64,
+                name: "num_parallel_calls");
+            variant_tensor = ops.parallel_map_dataset_v2(input_dataset.variant_tensor,
+                _num_parallel_calls,
+                func,
+                output_types,
+                output_shapes);
+        }
+    }
+}

src/TensorFlowNET.Core/Data/TensorDataset.cs

@@ -15,11 +15,9 @@ namespace Tensorflow
         public TensorDataset(Tensor feature, Tensor label)
         {
             _tensors = new[] { feature, label };
-            var batched_spec = _tensors.Select(x => x.ToTensorSpec()).ToArray();
-            structure = batched_spec.Select(x => x._unbatch()).ToArray();
+            structure = _tensors.Select(x => x.ToTensorSpec()).ToArray();
 
             variant_tensor = ops.tensor_dataset(_tensors, output_shapes);
-
         }
         public TensorDataset(Tensor element)
         {

src/TensorFlowNET.Core/Data/ZipDataset.cs

@@ -2,16 +2,19 @@
 using System.Collections.Generic;
 using System.Linq;
 using System.Text;
+using Tensorflow.Framework.Models;
 
 namespace Tensorflow
 {
     public class ZipDataset : DatasetV2
     {
-        dataset_ops ops = new dataset_ops();
         public ZipDataset(params IDatasetV2[] ds)
         {
             var input_datasets = ds.Select(x => x.variant_tensor).ToArray();
-            structure = ds.Select(x => x.structure[0]).ToArray();
+            var _structure = new List<TensorSpec>();
+            foreach (var dataset in ds)
+                _structure.AddRange(dataset.structure);
+            structure = _structure.ToArray();
             variant_tensor = ops.zip_dataset(input_datasets, output_types, output_shapes);
         }
     }