Merge branch 'SciSharp:master' into master

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

@@ -174,6 +174,20 @@ namespace Tensorflow
             return array_ops.reverse(tensor, axis, name: name);
         }
 
+
+        /// <summary>
+        /// Reverses specific dimensions of a tensor.
+        /// </summary>
+        /// <param name="tensor"></param>
+        /// <param name="axis"></param>
+        /// <param name="name"></param>
+        /// <returns></returns>
+        public Tensor reverse_v2(Tensor tensor, int[] axis, string name = null)
+            => gen_array_ops.reverse_v2(tensor, ops.convert_to_tensor(axis), name: name);
+
+        public Tensor reverse_v2(Tensor tensor, Tensor axis, string name = null)
+            => gen_array_ops.reverse_v2(tensor, axis, name: name);
+
         /// <summary>
         /// Returns the rank of a tensor.
         /// </summary>

test/TensorFlowNET.UnitTest/ManagedAPI/ArrayOpsTest.cs

@@ -4,6 +4,163 @@ using Tensorflow;
 using static Tensorflow.Binding;
 using System.Linq;
 
+namespace TensorFlowNET.UnitTest.ManagedAPI
+{
+    [TestClass]
+    public class ArrayOpsTest : EagerModeTestBase
+    {
+        /// <summary>
+        /// https://www.tensorflow.org/api_docs/python/tf/slice
+        /// </summary>
+        [TestMethod]
+        public void Slice()
+        {
+            // Tests based on example code in TF documentation
+            var input_array = tf.constant(np.array(new int[] { 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6 }).reshape((3, 2, 3)));
+            var indices = tf.constant(np.array(new int[] { 0, 2 }));
+
+            var r1 = array_ops.slice(input_array, ops.convert_n_to_tensor(new object[] { 1, 0, 0 }), ops.convert_n_to_tensor(new object[] { 1, 1, 3 }));
+            Assert.AreEqual(new Shape(1, 1, 3), r1.shape);
+            var r1np = r1.numpy();
+            Assert.AreEqual(r1np[0, 0, 0], 3);
+            Assert.AreEqual(r1np[0, 0, 1], 3);
+            Assert.AreEqual(r1np[0, 0, 2], 3);
+
+
+            var r2 = array_ops.slice(input_array, ops.convert_n_to_tensor(new object[] { 1, 0, 0 }), ops.convert_n_to_tensor(new object[] { 1, 2, 3 }));
+            Assert.AreEqual(new Shape(1, 2, 3), r2.shape);
+            var r2np = r2.numpy();
+            Assert.AreEqual(r2np[0, 0, 0], 3);
+            Assert.AreEqual(r2np[0, 0, 1], 3);
+            Assert.AreEqual(r2np[0, 0, 2], 3);
+            Assert.AreEqual(r2np[0, 1, 0], 4);
+            Assert.AreEqual(r2np[0, 1, 1], 4);
+            Assert.AreEqual(r2np[0, 1, 2], 4);
+
+            var r3 = array_ops.slice(input_array, ops.convert_n_to_tensor(new object[] { 1, 0, 0 }), ops.convert_n_to_tensor(new object[] { 2, 1, 3 }));
+            Assert.AreEqual(new Shape(2, 1, 3), r3.shape);
+            var r3np = r3.numpy();
+            Assert.AreEqual(r3np[0, 0, 0], 3);
+            Assert.AreEqual(r3np[0, 0, 1], 3);
+            Assert.AreEqual(r3np[0, 0, 2], 3);
+            Assert.AreEqual(r3np[1, 0, 0], 5);
+            Assert.AreEqual(r3np[1, 0, 1], 5);
+            Assert.AreEqual(r3np[1, 0, 2], 5);
+        }
+
+        /// <summary>
+        /// https://www.tensorflow.org/api_docs/python/tf/gather
+        /// </summary>
+        [TestMethod]
+        public void Gather()
+        {
+            var input_array = tf.constant(np.arange(12).reshape((3, 4)).astype(np.float32));
+            var indices = tf.constant(np.array(new int[] { 0, 2 }));
+
+            var result = array_ops.gather(input_array, indices);
+            Assert.AreEqual(new Shape(2, 4), result.shape);
+            Assert.AreEqual(result.numpy()[0, 0], 0.0f);
+            Assert.AreEqual(result.numpy()[0, 1], 1.0f);
+            Assert.AreEqual(result.numpy()[1, 3], 11.0f);
+
+            // Tests based on example code in Python doc string for tf.gather()
+
+            var p1 = tf.random.normal(new Shape(5, 6, 7, 8));
+            var i1 = tf.random_uniform(new Shape(10, 11), maxval: 7, dtype: tf.int32);
+            var r1 = tf.gather(p1, i1, axis: 2);
+            Assert.AreEqual(new Shape(5, 6, 10, 11, 8), r1.shape);
+
+            var p2 = tf.random.normal(new Shape(4, 3));
+            var i2 = tf.constant(new int[,] { { 0, 2 } });
+            var r2 = tf.gather(p2, i2, axis: 0);
+            Assert.AreEqual(new Shape(1, 2, 3), r2.shape);
+
+            var r3 = tf.gather(p2, i2, axis: 1);
+            Assert.AreEqual(new Shape(4, 1, 2), r3.shape);
+        }
+
+        /// <summary>
+        /// https://www.tensorflow.org/api_docs/python/tf/TensorArray
+        /// </summary>
+        [TestMethod]
+        public void TensorArray()
+        {
+            var ta = tf.TensorArray(tf.float32, size: 0, dynamic_size: true, clear_after_read: false);
+            ta.write(0, 10);
+            ta.write(1, 20);
+            ta.write(2, 30);
+            Assert.AreEqual(ta.read(0).numpy(), 10f);
+            Assert.AreEqual(ta.read(1).numpy(), 20f);
+            Assert.AreEqual(ta.read(2).numpy(), 30f);
+        }
+
+        /// <summary>
+        /// 
+        /// </summary>
+        [TestMethod]
+        public void Reverse()
+        {
+            /*
+             * python run get test data code:
+             
+import tensorflow as tf
+
+data=[[1, 2, 3], [4, 5, 6], [7,8,9]]
+
+data2 = tf.constant(data)
+
+print('test data shaper:', data2.shape)
+print('test data:', data2)
+
+axis = [-2,-1,0,1]
+for i in axis:
+    print('')
+    print('axis:', i)
+    ax = tf.constant([i])
+    datar = tf.reverse(data2, ax)
+    datar2 = array_ops.reverse(data2, ax)
+    print(datar)
+    print(datar2)
+
+             * */
+            var inputData = np.array(new int[,] { { 1, 2, 3 }, { 4, 5, 6 }, { 7, 8, 9 } });
+            var expectedOutput = new[] {
+            // np.array(new int[,] { { 7, 8, 9 }, { 4, 5, 6 }, { 1, 2, 3 } }),
+            np.array(new int[,] { { 3, 2, 1 }, { 6, 5, 4 }, { 9, 8, 7 } }),
+            np.array(new int[,] { { 7, 8, 9 }, { 4, 5, 6 }, { 1, 2, 3 } }),
+            np.array(new int[,] { { 3, 2, 1 }, { 6, 5, 4 }, { 9, 8, 7 } })
+        };
+
+            var axes = new int [] {  
+                -1, 
+                0,
+                1 };
+            for (var i = 0; i < axes.Length; i++)
+            {
+                var axis = axes[i];
+                var expected = tf.constant(expectedOutput[i]).numpy();
+
+                var inputTensor = tf.constant(inputData);
+                var axisTrensor = tf.constant(new[] { axis });
+
+                var outputTensor = tf.reverse_v2(inputTensor, axisTrensor);
+                var npout = outputTensor.numpy();
+                Assert.IsTrue(Enumerable.SequenceEqual(npout, expected), $"axis:{axis}");
+
+                var outputTensor2 = tf.reverse_v2(inputTensor, new[] { axis } );
+                var npout2 = outputTensor2.numpy();
+                Assert.IsTrue(Enumerable.SequenceEqual(npout2, expected), $"axis:{axis}");
+
+            }
+        }
+    }
+}
+using Microsoft.VisualStudio.TestTools.UnitTesting;
+using Tensorflow.NumPy;
+using Tensorflow;
+using static Tensorflow.Binding;
+using System.Linq;
+
 namespace TensorFlowNET.UnitTest.ManagedAPI
 {
     [TestClass]