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//
// TensorFlow.cs; Bindings to the TensorFlow C API for .NET
//
// Authors:
// Miguel de Icaza ([email protected])
//
// Strongly typed API
// The API generally takes a TF_Status that defaults to null, if the value is null, on error, this raises an exception, otherwise, the error is returned on the TF_Status.
// You can use TFStatus.Default for a value to use when you do not want to create the value yourself and are ok reusing the value.
//
// Guidaance on doing language bindings for Tensorflow:
// https://www.tensorflow.org/versions/r0.11/how_tos/language_bindings/
//
//
using System;
using System.Runtime.InteropServices;
using System.Text;
using System.Globalization;
using System.Linq;
// We use this TF_Xxx as the native "TF_Xxx *" as those are opaque
using TF_Status = System.IntPtr;
using TF_SessionOptions = System.IntPtr;
using TF_Graph = System.IntPtr;
using TF_OperationDescription = System.IntPtr;
using TF_Operation = System.IntPtr;
using TF_Session = System.IntPtr;
using TF_DeprecatedSession = System.IntPtr;
using TF_Tensor = System.IntPtr;
using TF_ImportGraphDefOptions = System.IntPtr;
using TF_Library = System.IntPtr;
using TF_BufferPtr = System.IntPtr;
using TF_Function = System.IntPtr;
using TF_DeviceList = System.IntPtr;
using size_t = System.UIntPtr;
using System.Numerics;
using System.Collections.Generic;
using System.Linq.Expressions;
namespace TensorFlow
{
static partial class NativeBinding
{
public const string TensorFlowLibrary = "libtensorflow";
public const string TensorFlowLibraryGPU = "libtensorflowgpu";
internal static string GetStr (this IntPtr x) => Marshal.PtrToStringAnsi (x);
}
/// <summary>
/// Contains TensorFlow fundamental methods and utility functions.
/// </summary>
public static class TFCore
{
internal static bool UseCPU = true;
[DllImport (NativeBinding.TensorFlowLibrary)]
static extern unsafe IntPtr TF_Version ();
static TFCore ()
{
Init ();
}
internal static void Init ()
{
CheckSize ();
}
/// <summary>
/// Returns the version of the TensorFlow runtime in use.
/// </summary>
/// <value>The version.</value>
public static string Version => TF_Version ().GetStr ();
// extern size_t TF_DataTypeSize (TF_DataType dt);
[DllImport (NativeBinding.TensorFlowLibrary)]
static extern IntPtr TF_DataTypeSize (TFDataType dt);
/// <summary>
/// Gets the size in bytes of the specified TensorFlow data type.
/// </summary>
/// <returns>The data type size.</returns>
/// <param name="dt">Dt.</param>
public static long GetDataTypeSize (TFDataType dt) => (long)TF_DataTypeSize (dt);
// extern TF_Buffer * TF_GetAllOpList ();
[DllImport (NativeBinding.TensorFlowLibrary)]
static extern unsafe IntPtr TF_GetAllOpList ();
/// <summary>
/// Retrieves the ProtocolBuffer describing all of the available operations in
/// the TensorFlow library in current use.
/// </summary>
/// <returns>The buffer contains a ProtocolBuffer encoded payload, you need a ProtocolBuffer reader to process the contents.</returns>
public static TFBuffer GetAllOpList ()
{
return new TFBuffer (TF_GetAllOpList ());
}
static void CheckSize ()
{
unsafe {
if (sizeof (IntPtr) == 4) {
Console.Error.WriteLine (
"The TensorFlow native libraries were compiled in 64 bit mode, you must run in 64 bit mode\n" +
"With Mono, do that with mono --arch=64 executable.exe, if using an IDE like MonoDevelop,\n" +
"Xamarin Studio or Visual Studio for Mac, Build/Compiler settings, make sure that " +
"\"Platform Target\" has x64 selected.");
throw new Exception ();
}
}
}
}
/// <summary>
/// Base class for many TensorFlow data types that provides a common idiom to dispose and
/// release resources associated with the native data types. Generally, you do not need to use this.
/// </summary>
/// <remarks>
/// <para>
/// This implements the Dispose pattern in a reusable form for TensorFlow types.
/// </para>
/// <para>
/// Subclasses invoke the constructor with the handle that this will wrap, and must
/// override the NativeDispose method (internal) to release the associated resource.
/// </para>
/// </remarks>
public abstract class TFDisposable : IDisposable
{
internal IntPtr handle;
/// <summary>
/// Returns the opaque handle to the object that this TFDisposable owns.
/// </summary>
/// <value>The handle.</value>
public IntPtr Handle => handle;
static TFDisposable ()
{
TFCore.Init ();
}
/// <summary>
/// Initializes a new instance of the <see cref="T:TensorFlow.TFDisposable"/> class.
/// </summary>
public TFDisposable ()
{ }
/// <summary>
/// Initializes a new instance of the <see cref="T:TensorFlow.TFDisposable"/> class
/// from the handle that it will wrap.
/// </summary>
public TFDisposable (IntPtr handle)
{
this.handle = handle;
}
/// <summary>
/// Releases all resource used by the <see cref="T:TensorFlow.TFDisposable"/> object.
/// </summary>
/// <remarks>Call Dispose when you are finished using the <see cref="T:TensorFlow.TFDisposable"/>. The
/// Dispose method leaves the <see cref="T:TensorFlow.TFDisposable"/> in an unusable state. After
/// calling Dispose, you must release all references to the <see cref="T:TensorFlow.TFDisposable"/> so
/// the garbage collector can reclaim the memory that the <see cref="T:TensorFlow.TFDisposable"/> was occupying.</remarks>
public void Dispose ()
{
Dispose (true);
GC.SuppressFinalize (this);
}
~TFDisposable ()
{
Dispose (false);
}
// Must be implemented in subclasses to dispose the unmanaged object, it does
// not need to take care of zeroing out the handle, that is done by the Dispose
// method inherited from TFDisposable
internal abstract void NativeDispose (IntPtr handle);
/// <summary>
/// Dispose the specified object
/// </summary>
/// <param name="disposing">If set to <c>true</c> it means that this method was called from Dispose, otherwise from the finalizer.</param>
public virtual void Dispose (bool disposing)
{
if (disposing) {
if (handle != IntPtr.Zero)
NativeDispose (handle);
handle = IntPtr.Zero;
}
}
internal static void ObjectDisposedException ()
{
throw new ObjectDisposedException ("The object was disposed");
}
}
/// <summary>
/// ase class for many TensorFlow data types that provides a common idiom to dispose and
/// release resources associated with the native data types and whose unmanaged resource
/// disposing can be called from a background thread (the finalizer). Users do not
/// need to deal with this class.
/// </summary>
/// <remarks>
/// Some object deletion APIs in TensorFlow can be invoked from a background thread,
/// so the release methods are suitable to be invoked from the Finalizer thread, in
/// those scenarios, subclass from this class rather than the TFDisposable class.
/// </remarks>
public abstract class TFDisposableThreadSafe : TFDisposable {
/// <summary>
/// Initializes a new instance of the <see cref="T:TensorFlow.TFDisposable"/> class
/// from the handle that it will wrap.
/// </summary>
public TFDisposableThreadSafe (IntPtr handle) : base (handle)
{
}
/// <summary>
/// Initializes a new instance of the <see cref="T:TensorFlow.TFDisposableThreadSafe"/> class.
/// </summary>
public TFDisposableThreadSafe ()
{ }
/// <summary>
/// Dispose the object, unlike the default implementat in TFDisposable,
/// this will release the unmanaged resources from a background thread.
/// </summary>
/// <param name="disposing">If set to <c>true</c> disposing.</param>
public override void Dispose (bool disposing)
{
if (handle != IntPtr.Zero)
NativeDispose (handle);
handle = IntPtr.Zero;
}
}
/// <summary>
/// TensorFlow Exception
/// </summary>
public class TFException : Exception {
/// <summary>
/// Initializes a new instance of the <see cref="T:TensorFlow.TFException"/> class with a message.
/// </summary>
/// <param name="message">Message.</param>
public TFException (string message) : base (message) { }
}
/// <summary>
/// Used to track the result of TensorFlow operations.
/// </summary>
/// <remarks>
/// <para>
/// TFStatus is used to track the status of a call to some TensorFlow
/// operations. Instances of this object are passed to various
/// TensorFlow operations and you can use the <see cref="P:TensorFlow.TFStatus.Ok"/>
/// to quickly check if the operation succeeded, or get more detail from the
/// <see cref="P:TensorFlow.TFStatus.StatusCode"/> and a human-readable text
/// using the <see cref="P:TensorFlow.TFStatus.StatusMessage"/> property.
/// </para>
/// <para>
/// The convenience <see cref="M:TensorFlow.TFStatus.Raise"/> can be used
/// to raise a <see cref="P:TensorFlow.TFException"/> if the status of the
/// operation did not succeed.
/// </para>
/// </remarks>
public class TFStatus : TFDisposable
{
// extern TF_Status * TF_NewStatus ();
[DllImport (NativeBinding.TensorFlowLibrary)]
internal static extern unsafe TF_Status TF_NewStatus ();
/// <summary>
/// Per-thread global status that you can use if you do not need to create a new instance of this object.
/// </summary>
/// <remarks>
/// This is provided as a convenience for APIs that take a TFStatus. While the TFStatus is usually an
/// optional parameter, when it is made optional, API calls that fail raise an exception. Use this
/// property to pass a TFStatus without having to allocate a new one. The problem with this of course
/// is that you risk having multiple parts of your code override this thread-global variable.
/// </remarks>
[ThreadStatic] public static TFStatus Default = new TFStatus ();
/// <summary>
/// Initializes a new instance of the <see cref="T:TensorFlow.TFStatus"/> class.
/// </summary>
public TFStatus () : base (TF_NewStatus ())
{
}
// extern void TF_DeleteStatus (TF_Status *);
[DllImport (NativeBinding.TensorFlowLibrary)]
internal static extern unsafe void TF_DeleteStatus (TF_Status status);
internal override void NativeDispose (IntPtr handle)
{
TF_DeleteStatus (handle);
}
// extern void TF_SetStatus (TF_Status *s, TF_Code code, const char *msg);
[DllImport (NativeBinding.TensorFlowLibrary)]
static extern unsafe void TF_SetStatus (TF_Status s, TFCode code, string msg);
/// <summary>
/// Sets the status code on this TFStatus.
/// </summary>
/// <param name="code">Code.</param>
/// <param name="msg">Message.</param>
public void SetStatusCode (TFCode code, string msg)
{
TF_SetStatus (handle, code, msg);
}
// extern TF_Code TF_GetCode (const TF_Status *s);
[DllImport (NativeBinding.TensorFlowLibrary)]
internal static extern unsafe TFCode TF_GetCode (TF_Status s);
/// <summary>
/// Gets the status code for the status code.
/// </summary>
/// <value>The status code as an enumeration.</value>
public TFCode StatusCode {
get {
if (handle == IntPtr.Zero)
throw new ObjectDisposedException ("TFStatus");
return TF_GetCode (handle);
}
}
// extern const char * TF_Message (const TF_Status *s);
[DllImport (NativeBinding.TensorFlowLibrary)]
static extern unsafe IntPtr TF_Message (TF_Status s);
/// <summary>
/// Gets a human-readable status message.
/// </summary>
/// <value>The status message.</value>
public string StatusMessage => TF_Message (handle).GetStr ();
/// <summary>
/// Returns a <see cref="T:System.String"/> that represents the current <see cref="T:TensorFlow.TFStatus"/>.
/// </summary>
/// <returns>A <see cref="T:System.String"/> that represents the current <see cref="T:TensorFlow.TFStatus"/>.</returns>
public override string ToString ()
{
if (handle == IntPtr.Zero)
throw new ObjectDisposedException ("TFStatus");
return string.Format ("[TFStatus: StatusCode={0}, StatusMessage={1}]", StatusCode, StatusMessage);
}
/// <summary>
/// Gets a value indicating whether this <see cref="T:TensorFlow.TFStatus"/> state has been set to ok.
/// </summary>
/// <value><c>true</c> if ok; otherwise, <c>false</c>.</value>
public bool Ok => StatusCode == TFCode.Ok;
/// <summary>
/// Gets a value indicating whether this <see cref="T:TensorFlow.TFStatus"/> state has been set to an error.
/// </summary>
/// <value><c>true</c> if error; otherwise, <c>false</c>.</value>
public bool Error => StatusCode != TFCode.Ok;
/// <summary>
/// Convenience method that raises an exception if the current status is an error.
/// </summary>
/// <remarks>
/// You can use this method as a convenience to raise an exception after you
/// invoke an operation if the operation did not succeed.
/// </remarks>
public void Raise ()
{
if (TF_GetCode (handle) != TFCode.Ok)
throw new TFException (StatusMessage);
}
//
// Utility function used to simplify implementing the idiom
// where the user optionally provides a TFStatus, if it is provided,
// the error is returned there; If it is not provided, then an
// exception is raised.
//
internal bool CheckMaybeRaise (TFStatus incomingStatus, bool last = true)
{
if (incomingStatus == null) {
if (handle == IntPtr.Zero)
Console.WriteLine ("oops");
if (StatusCode != TFCode.Ok) {
var e = new TFException (StatusMessage);
if (last)
Dispose ();
throw e;
}
if (last)
Dispose ();
return true;
}
return StatusCode == TFCode.Ok;
}
internal static TFStatus Setup (TFStatus incoming)
{
return incoming == null ? new TFStatus () : incoming;
}
}
internal class TFString
{
// extern size_t TF_StringEncode (const char *src, size_t src_len, char *dst, size_t dst_len, TF_Status *status);
[DllImport (NativeBinding.TensorFlowLibrary)]
internal static extern unsafe size_t TF_StringEncode (byte* src, size_t src_len, sbyte* dst, size_t dst_len, TF_Status status);
// extern size_t TF_StringDecode (const char *src, size_t src_len, const char **dst, size_t *dst_len, TF_Status *status);
[DllImport (NativeBinding.TensorFlowLibrary)]
internal static extern unsafe size_t TF_StringDecode (sbyte* src, size_t src_len, sbyte** dst, size_t* dst_len, TF_Status status);
// extern size_t TF_StringEncodedSize (size_t len);
[DllImport (NativeBinding.TensorFlowLibrary)]
internal static extern size_t TF_StringEncodedSize (size_t len);
}
/// <summary>
/// The session options object holds configuration options that you want to use during your session, like the TensorFlow target or the configuration.
/// </summary>
public class TFSessionOptions : TFDisposable
{
// extern TF_SessionOptions * TF_NewSessionOptions ();
[DllImport (NativeBinding.TensorFlowLibrary)]
internal static extern unsafe TF_SessionOptions TF_NewSessionOptions ();
/// <summary>
/// Initializes a new instance of the <see cref="T:TensorFlow.TFSessionOptions"/> class.
/// </summary>
public TFSessionOptions () : base (TF_NewSessionOptions ()) { }
// extern void TF_DeleteSessionOptions (TF_SessionOptions *);
[DllImport (NativeBinding.TensorFlowLibrary)]
internal static extern unsafe void TF_DeleteSessionOptions (TF_SessionOptions options);
internal override void NativeDispose (IntPtr handle)
{
TF_DeleteSessionOptions (handle);
}
// extern void TF_SetTarget (TF_SessionOptions *options, const char *target);
[DllImport (NativeBinding.TensorFlowLibrary)]
static extern unsafe void TF_SetTarget (TF_SessionOptions options, string target);
/// <summary>
/// Sets the target in options.
/// </summary>
/// <param name="target">target can be empty, a single entry, or a comma separated list of entries.
/// Each entry is in one of the following formats: "local", ip:port, host:port.</param>
///
public void SetTarget (string target)
{
if (handle == IntPtr.Zero)
ObjectDisposedException ();
TF_SetTarget (handle, target);
}
// extern void TF_SetConfig (TF_SessionOptions *options, const void *proto, size_t proto_len, TF_Status *status);
[DllImport (NativeBinding.TensorFlowLibrary)]
static extern unsafe void TF_SetConfig (TF_SessionOptions options, IntPtr proto, size_t proto_len, TF_Status status);
/// <summary>
/// Sets the configuration information for the session.
/// </summary>
/// <param name="protoData">Serialized protocol buffer for the tensorflow.ConfigProto message.</param>
/// <param name="length">Length of the buffer.</param>
/// <param name="status">If config was not parsed successfully as a ConfigProto, the error is recorded here.</param>
/// <remarks>
/// The configuration option is a Protocol Buffer representing the tensorflow.ConfigProto
/// </remarks>
public void SetConfig (IntPtr protoData, int length, TFStatus status = null)
{
if (handle == IntPtr.Zero)
ObjectDisposedException ();
var cstatus = TFStatus.Setup (status);
TF_SetConfig (handle, protoData, (UIntPtr)length, cstatus.handle);
cstatus.CheckMaybeRaise (status);
}
}
/// <summary>
/// Represents a computation graph. Graphs may be shared between sessions and are thread safe.
/// </summary>
/// <remarks>
/// <para>
/// Graphs consist of operations (represented by TFOperation objects), these can be named, or
/// the runtime will automatically assign a name.
/// </para>
/// <para>
/// For debugging purposes, you might want to group operations together, for this, call the
/// WithScope method with your new scope, which will create a new namespace for your object names.
/// </para>
/// <para>
/// For example, if you call WithScope ("demo"), and add an operation named "add" inside the
/// scope, the full name of the operation will be "demo/add", if you create a new scope inside, say
/// "hot", and add a "sub" operation there the result will be "demo/hot/sub".
/// </para>
/// </remarks>
public partial class TFGraph : TFDisposableThreadSafe
{
// extern TF_Graph * TF_NewGraph ();
[DllImport (NativeBinding.TensorFlowLibrary)]
static extern unsafe TF_Graph TF_NewGraph ();
/// <summary>
/// Initializes a new instance of the <see cref="T:TensorFlow.TFGraph"/> class.
/// </summary>
public TFGraph () : base (TF_NewGraph ())
{
}
internal TFGraph (IntPtr handle) : base (handle)
{
}
// extern void TF_DeleteGraph (TF_Graph *);
[DllImport (NativeBinding.TensorFlowLibrary)]
static extern unsafe void TF_DeleteGraph (TF_Graph graph);
internal override void NativeDispose (IntPtr handle)
{
TF_DeleteGraph (handle);
}
// extern void TF_GraphSetTensorShape (TF_Graph *graph, TF_Output output, const int64_t *dims, const int num_dims, TF_Status *status);
[DllImport (NativeBinding.TensorFlowLibrary)]
static extern unsafe void TF_GraphSetTensorShape (TF_Graph graph, TFOutput output, long [] dims, int num_dims, TF_Status status);
[DllImport (NativeBinding.TensorFlowLibrary)]
static extern unsafe void TF_GraphSetTensorShape (TF_Graph graph, TFOutput output, IntPtr dims, int num_dims, TF_Status status);
/// <summary>
/// Sets the tensor shape of the tensor referenced by <paramref name="output"/> to the shape described by <paramref name="dims"/>.
/// </summary>
/// <param name="output">The tensor on which this method will operate in the graph.</param>
/// <param name="dims">The tensor shape, specified as an array of dimensions.</param>
/// <param name="status">Status buffer, if specified a status code will be left here, if not specified, a <see cref="T:TensorFlow.TFException"/> exception is raised if there is an error.</param>
public void SetTensorShape (TFOutput output, long [] dims, TFStatus status = null)
{
if (handle == IntPtr.Zero)
ObjectDisposedException ();
var cstatus = TFStatus.Setup (status);
if (dims == null)
TF_GraphSetTensorShape (handle, output, IntPtr.Zero, 0, cstatus.handle);
else
TF_GraphSetTensorShape (handle, output, dims, dims.Length, cstatus.handle);
cstatus.CheckMaybeRaise (status);
}
// extern int TF_GraphGetTensorNumDims (TF_Graph *graph, TF_Output output, TF_Status *status);
[DllImport (NativeBinding.TensorFlowLibrary)]
static extern unsafe int TF_GraphGetTensorNumDims (TF_Graph graph, TFOutput output, TF_Status status);
/// <summary>
/// Returns the number of dimensions of the Tensor referenced by output
/// </summary>
/// <returns>The number of dimensions of the tensor.</returns>
/// <param name="output">The tensor to probe.</param>
/// <param name="status">Status buffer, if specified a status code will be left here, if not specified, a <see cref="T:TensorFlow.TFException"/> exception is raised if there is an error.</param>
public int GetTensorNumDims (TFOutput output, TFStatus status = null)
{
if (handle == IntPtr.Zero)
ObjectDisposedException ();
var cstatus = TFStatus.Setup (status);
var code = TF_GraphGetTensorNumDims (handle, output, cstatus.handle);
cstatus.CheckMaybeRaise (status);
return code;
}
// extern void TF_GraphGetTensorShape (TF_Graph *graph, TF_Output output, int64_t *dims, int num_dims, TF_Status *status);
[DllImport (NativeBinding.TensorFlowLibrary)]
static extern unsafe void TF_GraphGetTensorShape (TF_Graph graph, TFOutput output, long [] dims, int num_dims, TF_Status status);
/// <summary>
/// Returns the shape of a tensor specified in <paramref name="output"/>.
/// </summary>
///
/// <returns>The tensor shape. If the number of dimensions in the shape is unknown or the shape is, a scalar, the values in the array will be zero. Otherwise, each element of will be set corresponding to the size of the dimension. An unknown dimension is represented by -1.</returns>
/// <param name="output">The tensor that you want to look up. </param>
/// <param name="status">Status buffer, if specified a status code will be left here, if not specified, a <see cref="T:TensorFlow.TFException"/> exception is raised if there is an error.</param>
public TFShape GetTensorShape (TFOutput output, TFStatus status = null)
{
if (handle == IntPtr.Zero)
ObjectDisposedException ();
var cstatus = TFStatus.Setup (status);
var n = TF_GraphGetTensorNumDims (handle, output, cstatus.handle);
if (!cstatus.CheckMaybeRaise (status, last: false))
return TFShape.Unknown;
if (n == -1)
return TFShape.Unknown;
var dims = new long [n];
TF_GraphGetTensorShape (handle, output, dims, dims.Length, cstatus.handle);
cstatus.CheckMaybeRaise (status);
return new TFShape (dims);
}
// extern void TF_GraphToGraphDef (TF_Graph *graph, TF_Buffer *output_graph_def, TF_Status *status);
[DllImport (NativeBinding.TensorFlowLibrary)]
static extern unsafe void TF_GraphToGraphDef (TF_Graph graph, LLBuffer* output_graph_def, TF_Status status);
/// <summary>
/// Write out a serialized representation of the graph (as a GraphDef protocol buffer message) into <paramref name="outputGraphDef"/>.
/// </summary>
/// <param name="outputGraphDef">Target buffer where the graphs is serialized into.</param>
/// <param name="status">Status buffer, if specified a status code will be left here, if not specified, a <see cref="T:TensorFlow.TFException"/> exception is raised if there is an error.</param>
public void ToGraphDef (TFBuffer outputGraphDef, TFStatus status = null)
{
if (handle == IntPtr.Zero)
ObjectDisposedException ();
if (outputGraphDef == null)
throw new ArgumentNullException (nameof (outputGraphDef));
var cstatus = TFStatus.Setup (status);
unsafe
{
TF_GraphToGraphDef (handle, outputGraphDef.LLBuffer, cstatus.handle);
}
cstatus.CheckMaybeRaise (status);
}
// extern void TF_GraphImportGraphDef (TF_Graph *graph, const TF_Buffer *graph_def, const TF_ImportGraphDefOptions *options, TF_Status *status);
[DllImport (NativeBinding.TensorFlowLibrary)]
static extern unsafe void TF_GraphImportGraphDef (TF_Graph graph, LLBuffer* graph_def, TF_ImportGraphDefOptions options, TF_Status status);
/// <summary>
/// Import a serialized graph into this graph, using the specified prefix.
/// </summary>
/// <returns>The import.</returns>
/// <param name="graphDef">A buffer containing the serialized graph.</param>
/// <param name="prefix">A prefix that will be prepended to names of nodes in the <paramref name="graphDef"/> when they are imported into the graph.</param>
/// <param name="status">Status buffer, if specified a status code will be left here, if not specified, a <see cref="T:TensorFlow.TFException"/> exception is raised if there is an error.</param>
public void Import (TFBuffer graphDef, string prefix = "", TFStatus status = null)
{
if (handle == IntPtr.Zero)
ObjectDisposedException ();
if (graphDef == null)
throw new ArgumentNullException (nameof (graphDef));
if (prefix == null)
throw new ArgumentNullException (nameof (prefix));
using (var options = new TFImportGraphDefOptions ()) {
options.SetPrefix (prefix);
Import (graphDef, options, status);
}
}
/// <summary>
/// Import a serialized graph into this graph, using the specified importing options.
/// </summary>
/// <returns>The import.</returns>
/// <param name="graphDef">A buffer containing the serialized graph.</param>
/// <param name="options">Importing graph options.</param>
/// <param name="status">Status buffer, if specified a status code will be left here, if not specified, a <see cref="T:TensorFlow.TFException"/> exception is raised if there is an error.</param>
public void Import (TFBuffer graphDef, TFImportGraphDefOptions options, TFStatus status = null)
{
if (handle == IntPtr.Zero)
ObjectDisposedException ();
if (graphDef == null)
throw new ArgumentNullException (nameof (graphDef));
if (options == null)
throw new ArgumentNullException (nameof (options));
var cstatus = TFStatus.Setup (status);
unsafe
{
TF_GraphImportGraphDef (handle, graphDef.LLBuffer, options.handle, cstatus.handle);
}
cstatus.CheckMaybeRaise (status);
}
/// <summary>
/// Import a serialized graph held in a byte array into this graph, using the specified prefix.
/// </summary>
/// <returns>The import.</returns>
/// <param name="buffer">A byte array containing the serialized graph.</param>
/// <param name="prefix">A prefix that will be prepended to names of nodes in the graph when they are imported into the graph.</param>
/// <param name="status">Status buffer, if specified a status code will be left here, if not specified, a <see cref="T:TensorFlow.TFException"/> exception is raised if there is an error.</param>
public void Import (byte [] buffer, string prefix = "", TFStatus status = null)
{
if (handle == IntPtr.Zero)
ObjectDisposedException ();
if (buffer == null)
throw new ArgumentNullException (nameof (buffer));
if (prefix == null)
throw new ArgumentNullException (nameof (prefix));
using (var options = new TFImportGraphDefOptions ()) {
options.SetPrefix (prefix);
Import (buffer, options, status);
}
}
/// <summary>
/// Import a serialized graph held in a byte array into this graph, using the specified import options.
/// </summary>
/// <returns>The import.</returns>
/// <param name="buffer">A byte array containing the serialized graph.</param>
/// <param name="options">Importing graph options.</param>
/// <param name="status">Status buffer, if specified a status code will be left here, if not specified, a <see cref="T:TensorFlow.TFException"/> exception is raised if there is an error.</param>
/// <remarks>
/// If you are tryig to load a file stored using the SavedModel file format, you should use the <see cref="T:TensorFlow.TFSession.FromSavedModel"/> API instead.
/// </remarks>
public void Import (byte [] buffer, TFImportGraphDefOptions options, TFStatus status = null)
{
if (handle == IntPtr.Zero)
ObjectDisposedException ();
if (buffer == null)
throw new ArgumentNullException (nameof (buffer));
if (options == null)
throw new ArgumentNullException (nameof (options));
var cstatus = TFStatus.Setup (status);
using (var tb = new TFBuffer (buffer, 0, buffer.Length))
Import (tb, options, status);
cstatus.CheckMaybeRaise (cstatus);
}
// extern TF_Operation * TF_GraphOperationByName (TF_Graph *graph, const char *oper_name);
[DllImport (NativeBinding.TensorFlowLibrary)]
static extern unsafe TF_Operation TF_GraphOperationByName (TF_Graph graph, string oper_name);
/// <summary>
/// Gets the <see cref="T:TensorFlow.TFGraph"/> with the specified name, or null if the named operation does not exist in the graph.
/// </summary>
/// <param name="name">Name to lookup.</param>
public TFOperation this [string name] {
get {
if (handle == IntPtr.Zero)
ObjectDisposedException ();
var h = TF_GraphOperationByName (handle, name);
if (h == IntPtr.Zero)
return null;
return new TFOperation (this, h);
}
}
// extern TF_Operation * TF_GraphNextOperation (TF_Graph *graph, size_t *pos);
[DllImport (NativeBinding.TensorFlowLibrary)]
static extern unsafe TF_Operation TF_GraphNextOperation (TF_Graph graph, ref IntPtr token);
/// <summary>
/// Returns the enumerator that returns all the TFOperations in a graph.
/// </summary>
/// <returns>The enumerator.</returns>
public IEnumerable<TFOperation> GetEnumerator ()
{
if (handle == IntPtr.Zero)
ObjectDisposedException ();
IntPtr token = IntPtr.Zero;
IntPtr operll;
while ((operll = TF_GraphNextOperation (handle, ref token)) != IntPtr.Zero)
yield return new TFOperation (this, operll);
}
/// <summary>
/// Returns the tensor shape for the specific output pparameters as an array of longs.
/// </summary>
/// <returns>null for single dimension, .</returns>
/// <param name="output">The output operation to probe.</param>
/// <param name="status">Status buffer, if specified a status code will be left here, if not specified, a <see cref="T:TensorFlow.TFException"/> exception is raised if there is an error.</param>
public long [] GetShape (TFOutput output, TFStatus status = null)
{
if (handle == IntPtr.Zero)
ObjectDisposedException ();
var cstatus = TFStatus.Setup (status);
var ndims = TF_GraphGetTensorNumDims (handle, output, cstatus.handle);
if (!cstatus.CheckMaybeRaise (status, last: false))
return null;
if (ndims == 0)
return null;
var ret = new long [ndims];
TF_GraphGetTensorShape (handle, output, ret, ndims, cstatus.handle);
cstatus.CheckMaybeRaise (status);
return ret;
}
/// <summary>
/// Returns the current name scope in use, to change this, use the WithScope method.
/// </summary>
/// <value>The current name scope.</value>
public string CurrentNameScope { get; internal set; } = "";
/// <summary>
/// Creates a new namescope by setting the scope to the description provided.
/// </summary>
/// <returns>A new scope that will remain in use until the return TFScope is disposed.</returns>
/// <param name="nameScopeDesc">The namescope description, if the value is null, this
/// will reset the toplevel namescope to be the empty value. </param>
/// <remarks>
/// <para>
/// To more easily name your operations and group then, you can use the
/// WithScope method to set a current name scope that alter the complete name
/// of an operation added to the graph.
/// </para>
/// <para>
/// The graph starts with a scope set to the empty string, you can introduce new
/// scopes by calling WithScope, and can be conveniently used with the C# using
/// statement, like this:
/// </para>
/// <code>
/// Assert (graph.CurrentNamescope, "");
/// using (var nested = graph.WithScope ("nested")){
/// Assert (graph.CurrentNameScope, "nested");
/// using (var inner = graph.WithScope ("inner")){
/// Assert (graph.CurrentNameScope, "nested/inner");
/// }
/// }
/// </code>
/// </remarks>
public TFScope WithScope (string nameScopeDesc)
{
var scope = new TFScope (this);
if (CurrentNameScope.Length == 0)
CurrentNameScope = nameScopeDesc;
else
CurrentNameScope = CurrentNameScope + "/" + nameScopeDesc;
return scope;
}
/// <summary>
/// Returns the current variable dependencies in use. New tensors and operations will be created
/// with an added input dependency to the operations specified in this property. To change this,
/// use the WithDependencies method.
/// </summary>
/// <value>The current input dependencies to be used for new tensors and operations.</value>
public TFOperation [] CurrentDependencies { get; internal set; } = new TFOperation [0];
/// <summary>
/// Adds new dependencies for new tensors and operations created while the context is active.
/// </summary>
public TFDependencies WithDependencies (params TFOperation [] dependencies)
{
return new TFDependencies (this, dependencies);
}
Dictionary<string, int> values = new Dictionary<string, int> ();
internal string MakeName (string operName, string userName)
{
if (userName == null) {
var k = CurrentNameScope == "" ? operName : CurrentNameScope + "/" + operName;
return MakeUnique (k);
}
if (CurrentNameScope == "")
return userName;
return CurrentNameScope + "/" + userName;
}
string MakeUnique (string name)
{
int val = 0;
if (!values.TryGetValue (name, out val))
val = 0;
else
val++;
values [name] = val;
return name + val;
}
internal int LastId;
internal int GetNextId ()
{
return LastId++;
}
[DllImport (NativeBinding.TensorFlowLibrary)]
unsafe static extern void TF_GraphImportGraphDefWithReturnOutputs (TF_Graph graph, LLBuffer *graph_def, TF_ImportGraphDefOptions options, TFOutput *return_outputs, int num_return_outputs, TF_Status status);
/// <summary>
/// Imports a graph serialized into the graph
/// </summary>
/// <param name="graphDef">Serialized graph definition (in protocol buffer format).</param>
/// <param name="options">Import options.</param>
/// <param name="returnOutputs">Array large enough to contain all the return options.</param>
/// <param name="status">Status buffer, if specified a status code will be left here, if not specified, a <see cref="T:TensorFlow.TFException"/> exception is raised if there is an error.</param>
/// <remarks>
/// If you are tryig to load a file stored using the SavedModel file format, you should use the <see cref="T:TensorFlow.TFSession.FromSavedModel"/> API instead.
/// </remarks>
public void ImportGraphDef (TFBuffer graphDef, TFImportGraphDefOptions options, TFOutput [] returnOutputs, TFStatus status = null)
{
if (handle == IntPtr.Zero)
ObjectDisposedException ();
if (graphDef == null)
throw new ArgumentNullException (nameof (graphDef));
if (options == null)
throw new ArgumentNullException (nameof (options));
var cstatus = TFStatus.Setup (status);
unsafe
{
if (returnOutputs == null) {
TF_GraphImportGraphDefWithReturnOutputs (handle, graphDef.LLBuffer, options.handle, null, 0, cstatus.handle);
} else {
fixed (TFOutput* first = &returnOutputs [0])
{
TF_GraphImportGraphDefWithReturnOutputs (handle, graphDef.LLBuffer, options.handle, first, returnOutputs.Length, cstatus.handle);
}
}
}
}
[StructLayout (LayoutKind.Sequential)]
unsafe struct TFWhileParams
{
public int ninputs;
public TF_Graph cond_graph;
public TFOutput* cond_inputs;
public TFOutput cond_output;
public TF_Graph body_graph;
public TFOutput* body_inputs;
public TFOutput* body_outputs;
public IntPtr charPtrName;
}
[DllImport (NativeBinding.TensorFlowLibrary)]
static extern unsafe TFWhileParams TF_NewWhile (TF_Graph g, TFOutput [] inputs, int ninputs, TF_Status status);
[DllImport (NativeBinding.TensorFlowLibrary)]
static extern void TF_AbortWhile (ref TFWhileParams pars);
[DllImport (NativeBinding.TensorFlowLibrary)]
static extern unsafe void TF_FinishWhile (ref TFWhileParams pars, TF_Status status, TFOutput *outputs);
static unsafe TFOutput [] CopyFrom (TFOutput* ptr, int n)
{
var r = new TFOutput [n];
for (int i = 0; i < n; i++)
r [i] = ptr [i];
return r;
}
/// <summary>
/// Signature of the method that will be invoked by the TFGraph.While method to construct a while loop
/// </summary>
/// <remarks>
/// <para>
/// The method should build up the condition on the conditionGraph and the body of the while
/// loop in the provided bodyGraph. It should set the condOutput to the value used as the
/// condition output and the array of values in bodyOutputs to the final outputs as well as the
/// name to be used, if not set, one will be assigned.
/// </para>
/// <para>
/// The conditionGraph represents the while condition and the inputs are the current values of the
/// input variables (condInputs). The output should be a scalar boolean.
/// </para>
/// <para>
/// The loop body graph is in bodyGraph, The inputs are the current values of the loop
/// variables. The outputs are the updated values of the loop variables.
/// </para>
/// <para>
/// You can use the passed status record problems with it.
/// </para>
/// </remarks>
public delegate void WhileConstructor (TFGraph conditionGraph, TFOutput [] condInputs, out TFOutput condOutput, TFGraph bodyGraph, TFOutput [] bodyInputs, TFOutput [] bodyOutputs, out string name);
/// <summary>
/// Constructs a while loop with the specified inputs and a callback that composes the while loop
/// </summary>
/// <param name="inputs">Inputs.</param>
/// <param name="constructor">Callback method that fills out the various while loop parameters.</param>
/// <param name="status">Status buffer, if specified a status code will be left here, if not specified, a <see cref="T:TensorFlow.TFException"/> exception is raised if there is an error.</param>
/// <returns>
/// An array of TFOutputs from creating the While loop, or null if there is an error creating the
/// while loop, or if the constructor raised an exception when it was invoked.
/// </returns>
public TFOutput [] While (TFOutput [] inputs, WhileConstructor constructor, TFStatus status = null)
{
if (handle == IntPtr.Zero)
ObjectDisposedException ();
if (inputs == null)
throw new ArgumentNullException (nameof (inputs));
if (constructor == null)
throw new ArgumentNullException (nameof (constructor));
var cstatus = TFStatus.Setup (status);
TFWhileParams result = TF_NewWhile (handle, inputs, inputs.Length, cstatus.handle);
if (cstatus.Error)
return null;
try {
//