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Merge pull request #1207 from Wanglongzhi2001/concat_v2_test
test: add the concat_v2 test
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TensorFlow.Kernel.UnitTest/TensorFlow.Kernel.UnitTest.csproj
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| <Project Sdk="Microsoft.NET.Sdk"> | ||
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| <PropertyGroup> | ||
| <TargetFramework>net6.0</TargetFramework> | ||
| <ImplicitUsings>enable</ImplicitUsings> | ||
| <Nullable>enable</Nullable> | ||
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| <IsPackable>false</IsPackable> | ||
| <IsTestProject>true</IsTestProject> | ||
| </PropertyGroup> | ||
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| <ItemGroup> | ||
| <PackageReference Include="Microsoft.NET.Test.Sdk" Version="17.5.0" /> | ||
| <PackageReference Include="MSTest.TestAdapter" Version="2.2.10" /> | ||
| <PackageReference Include="MSTest.TestFramework" Version="2.2.10" /> | ||
| <PackageReference Include="coverlet.collector" Version="3.2.0" /> | ||
| </ItemGroup> | ||
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| <ItemGroup> | ||
| <ProjectReference Include="..\src\TensorFlowNET.Keras\Tensorflow.Keras.csproj" /> | ||
| <ProjectReference Include="..\tools\Tensorflow.UnitTest.RedistHolder\Tensorflow.UnitTest.RedistHolder.csproj" /> | ||
| </ItemGroup> | ||
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| </Project> |
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| using Microsoft.VisualStudio.TestTools.UnitTesting; | ||
| using Tensorflow; | ||
| using Tensorflow.NumPy; | ||
| using TensorFlow; | ||
| using static Tensorflow.Binding; | ||
| using static Tensorflow.KerasApi; | ||
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| namespace TensorFlow.Kernel.UnitTest | ||
| { | ||
| [TestClass] | ||
| public class concat_op_test | ||
| { | ||
| [TestMethod] | ||
| public void testConcatEmpty() | ||
| { | ||
| var t1 = tf.constant(new int[] { }); | ||
| var t2 = tf.constant(new int[] { }); | ||
| var c = array_ops.concat(new[] { t1, t2 }, 0); | ||
| var expected = np.array(new int[] { }); | ||
| Assert.IsTrue(Enumerable.SequenceEqual(expected.ToArray<int>(), c.numpy().ToArray<int>())); | ||
| } | ||
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| [TestMethod] | ||
| public void testConcatNegativeAxis() | ||
| { | ||
| var t1 = tf.constant(new int[,] {{ 1, 2, 3 }, { 4, 5, 6 } }); | ||
| var t2 = tf.constant(new int[,] { { 7, 8, 9 }, { 10, 11, 12 } }); | ||
| var c = array_ops.concat(new[] { t1, t2 }, -2); | ||
| var expected = np.array(new int[,,] { { { 1, 2, 3 }, { 4, 5, 6 } }, { { 7, 8, 9 }, { 10, 11, 12 } } }); | ||
| Assert.IsTrue(Enumerable.SequenceEqual(expected.ToArray<int>(), c.numpy().ToArray<int>())); | ||
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| c = array_ops.concat(new[] { t1, t2 }, -1); | ||
| expected = np.array(new int[,] { { 1, 2, 3, 7, 8, 9 }, { 4, 5, 6, 10, 11, 12 } }); | ||
| Assert.IsTrue(Enumerable.SequenceEqual(expected.ToArray<int>(), c.numpy().ToArray<int>())); | ||
| } | ||
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| [TestMethod] | ||
| [DataRow(TF_DataType.TF_INT32)] | ||
| [DataRow(TF_DataType.TF_INT64)] | ||
| [DataRow(TF_DataType.TF_UINT32)] | ||
| [DataRow(TF_DataType.TF_UINT64)] | ||
| public void testConcatDtype(TF_DataType dtype) | ||
| { | ||
| var t1 = tf.constant(new int[,] { { 1, 2, 3 }, { 4, 5, 6 } }, dtype: dtype); | ||
| var t2 = tf.constant(new int[,] { { 7, 8, 9 }, { 10, 11, 12 } }, dtype: dtype); | ||
| var c = array_ops.concat(new[] { t1, t2 }, 1); | ||
| var expected = np.array(new int[,] { { 1, 2, 3, 7, 8, 9 }, { 4, 5, 6, 10, 11, 12 } }); | ||
| Assert.IsTrue(Enumerable.SequenceEqual(expected.ToArray<int>(), tf.cast(c, TF_DataType.TF_INT32).numpy().ToArray<int>())); | ||
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| } | ||
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| [TestMethod] | ||
| [DataRow(TF_DataType.TF_INT32)] | ||
| [DataRow(TF_DataType.TF_INT64)] | ||
| public void testConcatAxisType(TF_DataType dtype) | ||
| { | ||
| var t1 = tf.constant(new int[,] { { 1, 2, 3 }, {4, 5, 6 } }); | ||
| var t2 = tf.constant(new int[,] { { 7, 8, 9 }, { 10, 11, 12 } }); | ||
| var c = array_ops.concat(new[] { t1, t2 }, tf.constant(1, dtype: dtype)); | ||
| var expected = np.array(new int[,] { { 1, 2, 3, 7, 8, 9 }, { 4, 5, 6, 10, 11, 12 } }); | ||
| Assert.IsTrue(Enumerable.SequenceEqual(expected.ToArray<int>(), tf.cast(c, TF_DataType.TF_INT32).numpy().ToArray<int>())); | ||
| } | ||
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| } | ||
| } |
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