Merge branch 'master' of https://github.com/SciSharp/TensorFlow.NET into alnovi/optimizer_tests

Author: novikov-alexander

README.md

@@ -15,20 +15,6 @@
 
 English | [中文](docs/README-CN.md)
 
-**=========================================================**
-
-### [Voting: Naming Convention Approach of v1.0.0](https://github.com/SciSharp/TensorFlow.NET/issues/1074)
-
-Dear all, 
-
-We would like to urge you to participate in our upcoming vote regarding the naming convention for TensorFlow.NET version 1.0.0 in [#1074](https://github.com/SciSharp/TensorFlow.NET/issues/1074). Your participation in the vote is essential to help us decide on the best approach for improving the naming convention used in previous versions.
-
-Thank you,
-
-TensorFlow.NET Authors
-
-**=========================================================**
-
 *master branch and v0.100.x is corresponding to tensorflow v2.10, v0.6x branch is from tensorflow v2.6, v0.15-tensorflow1.15 is from tensorflow1.15. Please add `https://www.myget.org/F/scisharp/api/v3/index.json` to nuget source to use nightly release.*
 
 
@@ -75,9 +61,12 @@ PM> Install-Package TensorFlow.Keras
 The second part is the computing support part. Only one of the following packages is needed, depending on your device and system.
 
 ```
-### CPU version for Windows, Linux and Mac
+### CPU version for Windows and Linux
 PM> Install-Package SciSharp.TensorFlow.Redist
 
+### CPU version for MacOS
+PM> Install-Package SciSharp.TensorFlow.Redist-OSX
+
 ### GPU version for Windows (CUDA and cuDNN are required)
 PM> Install-Package SciSharp.TensorFlow.Redist-Windows-GPU
 

TensorFlow.NET.sln

@@ -39,6 +39,8 @@ Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Tensorflow.Benchmark", "too
 EndProject
 Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Tensorflow.Console", "tools\TensorFlowNET.Console\Tensorflow.Console.csproj", "{1DC32255-BA1F-4D6D-A9C9-5BD5ED71CAA0}"
 EndProject
+Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "TensorFlow.Kernel.UnitTest", "test\TensorFlow.Kernel.UnitTest\TensorFlow.Kernel.UnitTest.csproj", "{654A027D-1364-4729-880B-144DFE1FF5BB}"
+EndProject
 Global
 	GlobalSection(SolutionConfigurationPlatforms) = preSolution
 		Debug|Any CPU = Debug|Any CPU
@@ -322,6 +324,24 @@ Global
 		{1DC32255-BA1F-4D6D-A9C9-5BD5ED71CAA0}.Release|x64.Build.0 = Release|x64
 		{1DC32255-BA1F-4D6D-A9C9-5BD5ED71CAA0}.Release|x86.ActiveCfg = Release|Any CPU
 		{1DC32255-BA1F-4D6D-A9C9-5BD5ED71CAA0}.Release|x86.Build.0 = Release|Any CPU
+		{654A027D-1364-4729-880B-144DFE1FF5BB}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
+		{654A027D-1364-4729-880B-144DFE1FF5BB}.Debug|Any CPU.Build.0 = Debug|Any CPU
+		{654A027D-1364-4729-880B-144DFE1FF5BB}.Debug|x64.ActiveCfg = Debug|Any CPU
+		{654A027D-1364-4729-880B-144DFE1FF5BB}.Debug|x64.Build.0 = Debug|Any CPU
+		{654A027D-1364-4729-880B-144DFE1FF5BB}.Debug|x86.ActiveCfg = Debug|Any CPU
+		{654A027D-1364-4729-880B-144DFE1FF5BB}.Debug|x86.Build.0 = Debug|Any CPU
+		{654A027D-1364-4729-880B-144DFE1FF5BB}.GPU|Any CPU.ActiveCfg = Debug|Any CPU
+		{654A027D-1364-4729-880B-144DFE1FF5BB}.GPU|Any CPU.Build.0 = Debug|Any CPU
+		{654A027D-1364-4729-880B-144DFE1FF5BB}.GPU|x64.ActiveCfg = Debug|Any CPU
+		{654A027D-1364-4729-880B-144DFE1FF5BB}.GPU|x64.Build.0 = Debug|Any CPU
+		{654A027D-1364-4729-880B-144DFE1FF5BB}.GPU|x86.ActiveCfg = Debug|Any CPU
+		{654A027D-1364-4729-880B-144DFE1FF5BB}.GPU|x86.Build.0 = Debug|Any CPU
+		{654A027D-1364-4729-880B-144DFE1FF5BB}.Release|Any CPU.ActiveCfg = Release|Any CPU
+		{654A027D-1364-4729-880B-144DFE1FF5BB}.Release|Any CPU.Build.0 = Release|Any CPU
+		{654A027D-1364-4729-880B-144DFE1FF5BB}.Release|x64.ActiveCfg = Release|Any CPU
+		{654A027D-1364-4729-880B-144DFE1FF5BB}.Release|x64.Build.0 = Release|Any CPU
+		{654A027D-1364-4729-880B-144DFE1FF5BB}.Release|x86.ActiveCfg = Release|Any CPU
+		{654A027D-1364-4729-880B-144DFE1FF5BB}.Release|x86.Build.0 = Release|Any CPU
 	EndGlobalSection
 	GlobalSection(SolutionProperties) = preSolution
 		HideSolutionNode = FALSE
@@ -342,6 +362,7 @@ Global
 		{D24FCAA5-548C-4251-B226-A1B6535D0845} = {E1A5D2B7-10AF-4876-85C0-7714EF274214}
 		{C23563DB-FE21-48E7-A411-87A109E4A899} = {E1A5D2B7-10AF-4876-85C0-7714EF274214}
 		{1DC32255-BA1F-4D6D-A9C9-5BD5ED71CAA0} = {E1A5D2B7-10AF-4876-85C0-7714EF274214}
+		{654A027D-1364-4729-880B-144DFE1FF5BB} = {1B0918B9-65AD-4F34-A287-AF4597B27DBD}
 	EndGlobalSection
 	GlobalSection(ExtensibilityGlobals) = postSolution
 		SolutionGuid = {2DEAD3CC-486B-4918-A607-50B0DE7B114A}

data/img001.bmp

@@ -8,10 +8,10 @@ namespace Tensorflow
     public partial class c_api
     {
         [DllImport(TensorFlowLibName)]
-        public static extern void TFC_SetAttr(SafeGraphHandle graph, IntPtr op, string attr_name, SafeBufferHandle attr_value_proto, SafeStatusHandle status);
+        public static extern void TF_SetAttr(SafeGraphHandle graph, IntPtr op, string attr_name, SafeBufferHandle attr_value_proto, SafeStatusHandle status);
         [DllImport(TensorFlowLibName)]
-        public static extern SafeBufferHandle TFC_GetHandleShapeAndType(SafeGraphHandle c_graph, TF_Output output);
+        public static extern SafeBufferHandle TF_GetHandleShapeAndType(SafeGraphHandle c_graph, TF_Output output);
         [DllImport(TensorFlowLibName)]
-        public static extern void TFC_SetHandleShapeAndType(SafeGraphHandle c_graph, TF_Output output, byte[] data, long proto_len, SafeStatusHandle status);
+        public static extern void TF_SetHandleShapeAndType(SafeGraphHandle c_graph, TF_Output output, byte[] data, long proto_len, SafeStatusHandle status);
     }
 }

src/TensorFlowNET.Core/APIs/c_api.customize.cs

@@ -140,6 +140,16 @@ public Tensor identity(Tensor input, string name = null)
         public Tensor gather(Tensor @params, Tensor indices, string name = null, int axis = 0)
             => array_ops.gather(@params, indices, name: name, axis: ops.convert_to_tensor(axis));
 
+        /// <summary>
+        /// Gather slices from `params` into a Tensor with shape specified by `indices`.
+        /// </summary>
+        /// <param name="params"></param>
+        /// <param name="indices"></param>
+        /// <param name="name"></param>
+        /// <returns></returns>
+        public Tensor gather_nd(Tensor @params, Tensor indices, string name = null)
+            => gen_array_ops.gather_nd(@params, indices, name: name);
+
         /// <summary>
         /// Return the elements, either from `x` or `y`, depending on the `condition`.
         /// </summary>

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

@@ -339,6 +339,13 @@ public Tensor decode_image(Tensor contents, int channels = 0, TF_DataType dtype
                 => image_ops_impl.decode_image(contents, channels: channels, dtype: dtype,
                     name: name, expand_animations: expand_animations);
 
+            public Tensor encode_png(Tensor contents, string name = null)
+                    => image_ops_impl.encode_png(contents, name: name);
+
+            public Tensor encode_jpeg(Tensor contents, string name = null)
+                    => image_ops_impl.encode_jpeg(contents, name: name);
+
+
             /// <summary>
             /// Convenience function to check if the 'contents' encodes a JPEG image.
             /// </summary>

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

@@ -16,6 +16,7 @@ limitations under the License.
 
 using System.Collections.Generic;
 using Tensorflow.IO;
+using Tensorflow.Operations;
 
 namespace Tensorflow
 {
@@ -46,6 +47,12 @@ public Operation save_v2(Tensor prefix, string[] tensor_names,
             public Tensor[] restore_v2(Tensor prefix, string[] tensor_names,
                 string[] shape_and_slices, TF_DataType[] dtypes, string name = null)
                 => ops.restore_v2(prefix, tensor_names, shape_and_slices, dtypes, name: name);
+
+            public Operation write_file(string filename, Tensor conentes, string name = null)
+                => write_file(Tensorflow.ops.convert_to_tensor(filename, TF_DataType.TF_STRING), conentes, name);
+
+            public Operation write_file(Tensor filename, Tensor conentes, string name = null)
+                => gen_ops.write_file(filename, conentes, name);
         }
 
         public GFile gfile = new GFile();

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

@@ -101,6 +101,8 @@ public Tensor embedding_lookup(Tensor @params,
                     name: name);
 
             public IActivation relu() => new relu();
+
+
             public IActivation swish() => new swish();
             public IActivation tanh() => new tanh();
 
@@ -111,6 +113,9 @@ public Tensor tanh(Tensor x, string name = null)
             public Tensor relu(Tensor features, string name = null)
                 => gen_nn_ops.relu(features, name);
 
+            public Tensor relu6(Tensor features, string name = null)
+                    => gen_nn_ops.relu6(features, name);
+
             public Tensor[] fused_batch_norm(Tensor x,
                 Tensor scale,
                 Tensor offset,

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

@@ -80,6 +80,11 @@ BackwardFunction GetGradientFunction(string op_name,
                      Tensor[] op_outputs)
             => (out_grads, unneeded_gradients) =>
             {
+                if(!ops.gradientFunctions.ContainsKey(op_name))
+                {
+                    throw new Exception($"gradientFunctions not find op_name: {op_name}");
+                }
+
                 if (ops.gradientFunctions[op_name] == null)
                     return new Tensor[op_inputs.Length];
 

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

@@ -381,5 +381,48 @@ public static Tensor[] _ReverseV2Grad(Operation op, Tensor[] grads)
             var axis = op.inputs[1];
             return new Tensor[] { array_ops.reverse(grad, axis), null };
         }
+
+        [RegisterGradient("Tile")]
+        public static Tensor[] _TileGrad(Operation op, Tensor[] grads)
+        {
+            var grad = grads[0];
+            var input_shape = array_ops.shape(op.inputs[0], out_type: op.inputs[1].dtype);
+            var split_shape = array_ops.reshape(array_ops.transpose(array_ops.stack(new Tensor[] { op.inputs[1], input_shape })), new Shape(-1));
+            var axes = math_ops.range(0, array_ops.size(split_shape), 2);
+
+            //# Sum reduces grad along the first dimension for IndexedSlices
+            //if isinstance(grad, indexed_slices_lib.IndexedSlices):
+            //input_shape_0 = math_ops.cast(input_shape[0], grad.indices.dtype)
+            //grad = math_ops.unsorted_segment_sum(
+            //    grad.values, math_ops.mod(grad.indices, input_shape_0), input_shape_0)
+            //split_shape = array_ops.concat([[1], split_shape[1:]], axis = 0)
+
+            var input_grad = math_ops.reduce_sum(array_ops.reshape(grad, split_shape), axes);
+            if (!tf.Context.executing_eagerly())
+            {
+                input_grad.set_shape(op.inputs[0].GetShape());
+            }
+            return new Tensor[] { input_grad, null };
+        }
+
+        [RegisterGradient("GatherNd")]
+        public static Tensor[] _GatherNdGrad(Operation op, Tensor[] grads)
+        {
+            var @ref = op.inputs[0];
+            var indices = op.inputs[1];
+            var grad = grads[0];
+            var ref_shape = array_ops.shape(@ref, out_type: indices.dtype);
+            Tensor ref_grad = null;
+            if (indices.shape.ndim == 2 && indices.shape.dims[indices.shape.Length - 1] == 1)
+            {
+                ref_grad = (Tensor)new IndexedSlices(grad, array_ops.squeeze(indices, axis: -1), ref_shape);
+            }
+            else
+            {
+                ref_grad = gen_array_ops.scatter_nd(indices, grad, ref_shape);
+            }
+            return new Tensor[] { ref_grad, null };
+        }
+
     }
 }