Updated Tensorflow.Net to 0.70.2 with Tensorflow 2.7.0.

Directory.Packages.props

@@ -42,8 +42,8 @@
     <PackageVersion Include="Parquet.Net" Version="$(ParquetDotNetVersion)" />
     <PackageVersion Include="Plotly.NET.CSharp" Version="$(PlotlyNETCSharpVersion)" />
     <PackageVersion Include="SciSharp.TensorFlow.Redist" Version="$(TensorFlowVersion)" />
-    <PackageVersion Include="SciSharp.TensorFlow.Redist-Linux-GPU" Version="$(TensorFlowVersion)" />
-    <PackageVersion Include="SciSharp.TensorFlow.Redist-Windows-GPU" Version="$(TensorFlowVersion)" />
+    <PackageVersion Include="SciSharp.TensorFlow.Redist-Linux-GPU" Version="$(TensorFlowLinuxGpuVersion)" />
+    <PackageVersion Include="SciSharp.TensorFlow.Redist-Windows-GPU" Version="$(TensorFlowWindowsGpuVersion)" />
     <PackageVersion Include="SharpZipLib" Version="$(SharpZipLibVersion)" />
     <PackageVersion Include="SkiaSharp" Version="$(SkiaSharpVersion)" />
     <PackageVersion Include="SkiaSharp.NativeAssets.Linux.NoDependencies" Version="$(SkiaSharpVersion)" />

docs/samples/Microsoft.ML.Samples.GPU/Microsoft.ML.Samples.GPU.csproj

@@ -53,22 +53,27 @@
     <PackageReference Include="SciSharp.TensorFlow.Redist-Linux-GPU" />
   </ItemGroup>
 
-    <ItemGroup>
+  <!-- workaround https://github.com/SciSharp/TensorFlow.NET/issues/1297 -->
+  <Target Name="_EnsureObjFolder" BeforeTargets="FileRestitch">
+    <MakeDir Directories="obj" />
+  </Target>
+
+  <ItemGroup>
     <Content Include="$(ModelDownloadPath)\ResNet18Onnx\ResNet18.onnx">
       <Link>DnnImageModels\ResNet18Onnx\ResNet18.onnx</Link>
       <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
     </Content>
   </ItemGroup>
 
-    <ItemGroup>
+ <ItemGroup>
     <Content Include="$(ModelDownloadPath)\ResNetPrepOnnx\ResNetPreprocess.onnx">
       <Link>DnnImageModels\ResNetPrepOnnx\ResNetPreprocess.onnx</Link>
       <CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
     </Content>
   </ItemGroup>
 
-    <ItemGroup>
-      <Folder Include="Dynamic\" />
-    </ItemGroup>
+  <ItemGroup>
+    <Folder Include="Dynamic\" />
+  </ItemGroup>
 
 </Project>

eng/Versions.props

@@ -67,9 +67,13 @@
     <ParquetDotNetVersion>2.1.3</ParquetDotNetVersion>
     <PlotlyNETCSharpVersion>0.11.1</PlotlyNETCSharpVersion>
     <SharpZipLibVersion>1.4.2</SharpZipLibVersion>
-    <TensorflowDotNETVersion>0.20.1</TensorflowDotNETVersion>
+    <!-- TensorFlow.NET cannot be updated past 0.100.2 due to https://github.com/SciSharp/TensorFlow.NET/issues/1296, 
+     and regression https://github.com/SciSharp/TensorFlow.NET/commit/a7c9a75954d219cb606042fcbfbeb1b176781d7e introduced in 0.100.4 -->
+    <TensorflowDotNETVersion>0.100.2</TensorflowDotNETVersion>
     <TensorFlowMajorVersion>2</TensorFlowMajorVersion>
-    <TensorFlowVersion>2.3.1</TensorFlowVersion>
+    <TensorFlowVersion>2.16.0</TensorFlowVersion>
+    <TensorFlowWindowsGpuVersion>2.10.3</TensorFlowWindowsGpuVersion>
+    <TensorFlowLinuxGpuVersion>2.11.1</TensorFlowLinuxGpuVersion>
     <TorchSharpPyBridgeVersion>1.4.1</TorchSharpPyBridgeVersion>
     <AutoGenVersion>0.2.3</AutoGenVersion>
     <SemanticKernelVersion>1.48.0</SemanticKernelVersion>

src/Microsoft.ML.TensorFlow/TensorTypeExtensions.cs

@@ -25,7 +25,7 @@ public static void ToScalar<T>(this Tensor tensor, ref T dst) where T : unmanage
                 return;
             }
 
-            if (typeof(T).as_dtype() != tensor.dtype)
+            if (typeof(T).as_tf_dtype() != tensor.dtype)
                 throw new NotSupportedException();
 
             unsafe
@@ -37,7 +37,7 @@ public static void ToScalar<T>(this Tensor tensor, ref T dst) where T : unmanage
 
         public static void CopyTo<T>(this Tensor tensor, Span<T> values) where T : unmanaged
         {
-            if (typeof(T).as_dtype() != tensor.dtype)
+            if (typeof(T).as_tf_dtype() != tensor.dtype)
                 throw new NotSupportedException();
 
             unsafe

src/Microsoft.ML.TensorFlow/TensorflowTransform.cs

@@ -16,8 +16,8 @@
 using Microsoft.ML.Runtime;
 using Microsoft.ML.TensorFlow;
 using Microsoft.ML.Transforms;
-using NumSharp;
 using Tensorflow;
+using Tensorflow.NumPy;
 using static Microsoft.ML.TensorFlow.TensorFlowUtils;
 using static Tensorflow.Binding;
 using Utils = Microsoft.ML.Internal.Utilities.Utils;
@@ -51,7 +51,7 @@ public sealed class TensorFlowTransformer : RowToRowTransformerBase, IDisposable
         internal readonly DataViewType[] OutputTypes;
         internal readonly TF_DataType[] TFOutputTypes;
         internal readonly TF_DataType[] TFInputTypes;
-        internal readonly TensorShape[] TFInputShapes;
+        internal readonly Shape[] TFInputShapes;
         internal readonly (Operation, int)[] TFInputOperations;
         internal readonly (Operation, int)[] TFOutputOperations;
         internal TF_Output[] TFInputNodes;
@@ -212,14 +212,14 @@ internal TensorFlowTransformer(IHostEnvironment env, TensorFlowEstimator.Options
             env.CheckValue(options, nameof(options));
         }
 
-        private static ITensorValueGetter CreateTensorValueGetter<T>(DataViewRow input, bool isVector, int colIndex, TensorShape tfShape)
+        private static ITensorValueGetter CreateTensorValueGetter<T>(DataViewRow input, bool isVector, int colIndex, Shape tfShape)
         {
             if (isVector)
                 return new TensorValueGetterVec<T>(input, colIndex, tfShape);
             return new TensorValueGetter<T>(input, colIndex, tfShape);
         }
 
-        private static ITensorValueGetter CreateTensorValueGetter(DataViewRow input, TF_DataType tfType, bool isVector, int colIndex, TensorShape tfShape)
+        private static ITensorValueGetter CreateTensorValueGetter(DataViewRow input, TF_DataType tfType, bool isVector, int colIndex, Shape tfShape)
         {
             var type = Tf2MlNetType(tfType);
             return Utils.MarshalInvoke(CreateTensorValueGetter<int>, type.RawType, input, isVector, colIndex, tfShape);
@@ -230,7 +230,7 @@ private static ITensorValueGetter[] GetTensorValueGetters(
             int[] inputColIndices,
             bool[] isInputVector,
             TF_DataType[] tfInputTypes,
-            TensorShape[] tfInputShapes)
+            Shape[] tfInputShapes)
         {
             var srcTensorGetters = new ITensorValueGetter[inputColIndices.Length];
             for (int i = 0; i < inputColIndices.Length; i++)
@@ -331,10 +331,10 @@ private static (Operation, int) GetOperationFromName(string operation, Session s
             return (session.graph.OperationByName(operation), 0);
         }
 
-        internal static (TF_DataType[] tfInputTypes, TensorShape[] tfInputShapes, (Operation, int)[]) GetInputInfo(IHost host, Session session, string[] inputs, int batchSize = 1)
+        internal static (TF_DataType[] tfInputTypes, Shape[] tfInputShapes, (Operation, int)[]) GetInputInfo(IHost host, Session session, string[] inputs, int batchSize = 1)
         {
             var tfInputTypes = new TF_DataType[inputs.Length];
-            var tfInputShapes = new TensorShape[inputs.Length];
+            var tfInputShapes = new Shape[inputs.Length];
             var tfInputOperations = new (Operation, int)[inputs.Length];
 
             int index = 0;
@@ -351,15 +351,15 @@ internal static (TF_DataType[] tfInputTypes, TensorShape[] tfInputShapes, (Opera
                     throw host.ExceptParam(nameof(session), $"Input type '{tfInputType}' of input column '{input}' is not supported in TensorFlow");
 
                 tfInputTypes[index] = tfInputType;
-                tfInputShapes[index] = ((Tensor)inputTensor).TensorShape;
+                tfInputShapes[index] = ((Tensor)inputTensor).shape;
                 tfInputOperations[index] = (inputTensor, inputTensorIndex);
                 index++;
             }
 
             return (tfInputTypes, tfInputShapes, tfInputOperations);
         }
 
-        internal static TensorShape GetTensorShape(TF_Output output, Graph graph, Status status = null)
+        internal static Shape GetTensorShape(TF_Output output, Graph graph, Status status = null)
         {
             if (graph == IntPtr.Zero)
                 throw new ObjectDisposedException(nameof(graph));
@@ -370,12 +370,12 @@ internal static TensorShape GetTensorShape(TF_Output output, Graph graph, Status
             cstatus.Check();
 
             if (n == -1)
-                return new TensorShape(new int[0]);
+                return new Shape(new int[0]);
 
             var dims = new long[n];
             c_api.TF_GraphGetTensorShape(graph, output, dims, dims.Length, cstatus.Handle);
             cstatus.Check();
-            return new TensorShape(dims.Select(x => (int)x).ToArray());
+            return new Shape(dims.Select(x => (int)x).ToArray());
         }
 
         internal static (TF_DataType[] tfOutputTypes, DataViewType[] outputTypes, (Operation, int)[]) GetOutputInfo(IHost host, Session session, string[] outputs, bool treatOutputAsBatched)
@@ -402,7 +402,7 @@ internal static (TF_DataType[] tfOutputTypes, DataViewType[] outputTypes, (Opera
                 // i.e. the first dimension (if unknown) is assumed to be batch dimension.
                 // If there are other dimension that are unknown the transformer will return a variable length vector.
                 // This is the work around in absence of reshape transformer.
-                var idims = shape.dims;
+                var idims = shape.dims?.Select(x => checked((int)x))?.ToArray();
 
                 int[] dims = idims;
                 if (treatOutputAsBatched)
@@ -517,7 +517,7 @@ public void Dispose()
 
                 if (Session != null && Session != IntPtr.Zero)
                 {
-                    Session.close(); // invoked Dispose()
+                    Session.Dispose();
                 }
             }
             finally
@@ -536,7 +536,7 @@ private sealed class Mapper : MapperBase
             private readonly TensorFlowTransformer _parent;
             private readonly int[] _inputColIndices;
             private readonly bool[] _isInputVector;
-            private readonly TensorShape[] _fullySpecifiedShapes;
+            private readonly Shape[] _fullySpecifiedShapes;
             private readonly ConcurrentBag<Runner> _runners;
 
             public Mapper(TensorFlowTransformer parent, DataViewSchema inputSchema) :
@@ -546,7 +546,7 @@ public Mapper(TensorFlowTransformer parent, DataViewSchema inputSchema) :
                 _parent = parent;
                 _inputColIndices = new int[_parent.Inputs.Length];
                 _isInputVector = new bool[_parent.Inputs.Length];
-                _fullySpecifiedShapes = new TensorShape[_parent.Inputs.Length];
+                _fullySpecifiedShapes = new Shape[_parent.Inputs.Length];
                 for (int i = 0; i < _parent.Inputs.Length; i++)
                 {
                     if (!inputSchema.TryGetColumnIndex(_parent.Inputs[i], out _inputColIndices[i]))
@@ -570,19 +570,19 @@ public Mapper(TensorFlowTransformer parent, DataViewSchema inputSchema) :
                         {
                             vecType = (VectorDataViewType)type;
                             var colTypeDims = vecType.Dimensions.Select(dim => (int)dim).ToArray();
-                            _fullySpecifiedShapes[i] = new TensorShape(colTypeDims);
+                            _fullySpecifiedShapes[i] = new Shape(colTypeDims);
                         }
                         else
                             // for primitive type use default TensorShape
-                            _fullySpecifiedShapes[i] = new TensorShape();
+                            _fullySpecifiedShapes[i] = new Shape(Array.Empty<long>());
                     }
                     else
                     {
                         vecType = (VectorDataViewType)type;
                         var colTypeDims = vecType.Dimensions.Select(dim => (int)dim).ToArray();
                         // If the column is one dimension we make sure that the total size of the TF shape matches.
                         // Compute the total size of the known dimensions of the shape.
-                        int valCount = 1;
+                        long valCount = 1;
                         int numOfUnkDim = 0;
                         foreach (var s in shape)
                         {
@@ -592,7 +592,7 @@ public Mapper(TensorFlowTransformer parent, DataViewSchema inputSchema) :
                                 numOfUnkDim++;
                         }
                         // The column length should be divisible by this, so that the other dimensions can be integral.
-                        int typeValueCount = type.GetValueCount();
+                        long typeValueCount = type.GetValueCount();
                         if (typeValueCount % valCount != 0)
                             throw Contracts.Except($"Input shape mismatch: Input '{_parent.Inputs[i]}' has shape {originalShape.ToString()}, but input data is of length {typeValueCount}.");
 
@@ -616,22 +616,22 @@ public Mapper(TensorFlowTransformer parent, DataViewSchema inputSchema) :
                             throw Contracts.Except($"Input shape mismatch: Input '{_parent.Inputs[i]}' has shape {originalShape.ToString()}, but input data is of length {typeValueCount}.");
 
                         // Fill in the unknown dimensions.
-                        var l = new int[originalShapeNdim];
+                        var l = new long[originalShapeNdim];
                         for (int ishape = 0; ishape < originalShapeNdim; ishape++)
-                            l[ishape] = originalShapeDims[ishape] == -1 ? (int)d : originalShapeDims[ishape];
-                        _fullySpecifiedShapes[i] = new TensorShape(l);
+                            l[ishape] = originalShapeDims[ishape] == -1 ? (long)d : originalShapeDims[ishape];
+                        _fullySpecifiedShapes[i] = new Shape(l);
                     }
 
                     if (_parent._addBatchDimensionInput)
                     {
                         // ndim of default TensorShape is -1, make originDim to 0 in this case.
                         // after addBatchDimension, input column will be changed: type -> type[]
                         var originDim = _fullySpecifiedShapes[i].ndim < 0 ? 0 : _fullySpecifiedShapes[i].ndim;
-                        var l = new int[originDim + 1];
+                        var l = new long[originDim + 1];
                         l[0] = 1;
                         for (int ishape = 1; ishape < l.Length; ishape++)
                             l[ishape] = _fullySpecifiedShapes[i].dims[ishape - 1];
-                        _fullySpecifiedShapes[i] = new TensorShape(l);
+                        _fullySpecifiedShapes[i] = new Shape(l);
                     }
                 }
 
@@ -720,7 +720,7 @@ private Delegate MakeGetter<T>(DataViewRow input, int iinfo, ITensorValueGetter[
                             UpdateCacheIfNeeded(input.Position, srcTensorGetters, activeOutputColNames, outputCache);
 
                             var tensor = outputCache.Outputs[_parent.Outputs[iinfo]];
-                            var tensorSize = tensor.TensorShape.dims.Where(x => x > 0).Aggregate((x, y) => x * y);
+                            var tensorSize = tensor.shape.dims.Where(x => x > 0).Aggregate((x, y) => x * y);
 
                             var editor = VBufferEditor.Create(ref dst, (int)tensorSize);
                             FetchStringData(tensor, editor.Values);
@@ -735,7 +735,7 @@ private Delegate MakeGetter<T>(DataViewRow input, int iinfo, ITensorValueGetter[
                             UpdateCacheIfNeeded(input.Position, srcTensorGetters, activeOutputColNames, outputCache);
 
                             var tensor = outputCache.Outputs[_parent.Outputs[iinfo]];
-                            var tensorSize = tensor.TensorShape.dims.Where(x => x > 0).Aggregate((x, y) => x * y);
+                            var tensorSize = tensor.shape.dims.Where(x => x > 0).Aggregate((x, y) => x * y);
 
                             var editor = VBufferEditor.Create(ref dst, (int)tensorSize);
 
@@ -821,10 +821,10 @@ private class TensorValueGetter<T> : ITensorValueGetter
         {
             private readonly ValueGetter<T> _srcgetter;
             private readonly T[] _bufferedData;
-            private readonly TensorShape _tfShape;
+            private readonly Shape _tfShape;
             private int _position;
 
-            public TensorValueGetter(DataViewRow input, int colIndex, TensorShape tfShape)
+            public TensorValueGetter(DataViewRow input, int colIndex, Shape tfShape)
             {
                 _srcgetter = input.GetGetter<T>(input.Schema[colIndex]);
                 _tfShape = tfShape;
@@ -864,15 +864,15 @@ public Tensor GetBufferedBatchTensor()
         private class TensorValueGetterVec<T> : ITensorValueGetter
         {
             private readonly ValueGetter<VBuffer<T>> _srcgetter;
-            private readonly TensorShape _tfShape;
+            private readonly Shape _tfShape;
             private VBuffer<T> _vBuffer;
             private T[] _denseData;
             private T[] _bufferedData;
             private int _position;
             private readonly long[] _dims;
             private readonly long _bufferedDataSize;
 
-            public TensorValueGetterVec(DataViewRow input, int colIndex, TensorShape tfShape)
+            public TensorValueGetterVec(DataViewRow input, int colIndex, Shape tfShape)
             {
                 _srcgetter = input.GetGetter<VBuffer<T>>(input.Schema[colIndex]);
                 _tfShape = tfShape;