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added GraphTests, all Igored at the moment
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‎src/TensorFlowNET.Core/Graphs/Graph.cs‎

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@@ -12,6 +12,51 @@ namespace Tensorflow
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/// then create a TensorFlow session to run parts of the graph across a set of local and remote devices.
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/// https://www.tensorflow.org/guide/graphs
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/// </summary>
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/*
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A TensorFlow computation, represented as a dataflow graph.
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A `Graph` contains a set of
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`tf.Operation` objects,
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which represent units of computation; and
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`tf.Tensor` objects, which represent
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the units of data that flow between operations.
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A default `Graph` is always registered, and accessible by calling
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`tf.get_default_graph`.
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To add an operation to the default graph, simply call one of the functions
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that defines a new `Operation`:
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```python
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c = tf.constant(4.0)
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assert c.graph is tf.get_default_graph()
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```
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Another typical usage involves the
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`tf.Graph.as_default`
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context manager, which overrides the current default graph for the
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lifetime of the context:
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```python
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g = tf.Graph()
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with g.as_default():
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# Define operations and tensors in `g`.
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c = tf.constant(30.0)
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assert c.graph is g
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```
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Important note: This class *is not* thread-safe for graph construction. All
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operations should be created from a single thread, or external
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synchronization must be provided. Unless otherwise specified, all methods
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are not thread-safe.
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A `Graph` instance supports an arbitrary number of "collections"
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that are identified by name. For convenience when building a large
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graph, collections can store groups of related objects: for
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example, the `tf.Variable` uses a collection (named
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`tf.GraphKeys.GLOBAL_VARIABLES`) for
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all variables that are created during the construction of a graph. The caller
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may define additional collections by specifying a new name.
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*/
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public partial class Graph : IPython, IDisposable
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{
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private IntPtr _handle;

‎src/TensorFlowNET.Core/ops.py.cs‎

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@@ -49,19 +49,59 @@ public static object get_collection_ref(string key)
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return get_default_graph().get_collection_ref(key);
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}
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private static Graph default_graph;
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private static Graph default_graph;
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/// <summary>
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/// Returns the default graph for the current thread.
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///
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/// The returned graph will be the innermost graph on which a
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/// `Graph.as_default()` context has been entered, or a global default
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/// graph if none has been explicitly created.
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///
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/// NOTE: The default graph is a property of the current thread.If you
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/// create a new thread, and wish to use the default graph in that
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/// thread, you must explicitly add a `with g.as_default():` in that
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/// thread's function.
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/// </summary>
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/// <returns></returns>
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public static Graph get_default_graph()
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{
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//TODO: original source indicates there should be a _default_graph_stack!
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//return _default_graph_stack.get_default()
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if (default_graph == null)
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default_graph = tf.Graph();
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return default_graph;
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}
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public static Graph set_default_graph(Graph graph)
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{
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//TODO: original source does not have a 'set_default_graph' and indicates there should be a _default_graph_stack!
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default_graph = graph;
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return default_graph;
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}
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/// <summary>
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/// Clears the default graph stack and resets the global default graph.
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///
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/// NOTE: The default graph is a property of the current thread.This
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/// function applies only to the current thread.Calling this function while
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/// a `tf.Session` or `tf.InteractiveSession` is active will result in undefined
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/// behavior. Using any previously created `tf.Operation` or `tf.Tensor` objects
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/// after calling this function will result in undefined behavior.
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/// </summary>
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/// <returns></returns>
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public static void reset_default_graph()
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{
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//TODO: original source indicates there should be a _default_graph_stack!
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//if (!_default_graph_stack.is_cleared())
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// throw new InvalidOperationException("Do not use tf.reset_default_graph() to clear " +
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// "nested graphs. If you need a cleared graph, " +
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// "exit the nesting and create a new graph.");
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//_default_graph_stack.reset();
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if (default_graph!=null)
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default_graph.Dispose();
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default_graph = tf.Graph();
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}
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public static Graph _get_graph_from_inputs(List<Tensor> op_input_list, Graph graph = null)
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{
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foreach(var op_input in op_input_list)

‎test/TensorFlowNET.UnitTest/ops_test/GraphTest.cs‎

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using System;
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using System.Collections.Generic;
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using System.Linq;
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using System.Text;
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using Microsoft.VisualStudio.TestTools.UnitTesting;
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using Tensorflow;
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using Tensorflow.Operations;
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namespace TensorFlowNET.UnitTest.ops_test
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{
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/// <summary>
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/// excerpt of tensorflow/python/framework/ops_test.py
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/// </summary>
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[TestClass]
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public class GraphTest : PythonTest
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{
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[TestInitialize]
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public void SetUp()
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{
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ops.reset_default_graph();
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}
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[TestCleanup]
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public void TearDown()
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{
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ops.reset_default_graph();
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}
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private void _AssertDefault(Graph expected) {
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Assert.AreSame(ops.get_default_graph(), expected);
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}
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[Ignore("Todo: Port")]
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[TestMethod]
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public void testResetDefaultGraphNesting()
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{
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/*
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def testResetDefaultGraphNesting(self):
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g0 = ops.Graph()
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with self.assertRaises(AssertionError):
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with g0.as_default():
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ops.reset_default_graph()
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*/
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}
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[Ignore("Todo: Port")]
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[TestMethod]
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public void testGraphContextManagerCancelsEager()
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{
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/*
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def testGraphContextManagerCancelsEager(self):
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with context.eager_mode():
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with ops.Graph().as_default():
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self.assertFalse(context.executing_eagerly())
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*/
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}
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[Ignore("Todo: Port")]
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[TestMethod]
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public void testGraphContextManager()
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{
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/*
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def testGraphContextManager(self):
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g0 = ops.Graph()
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with g0.as_default() as g1:
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self.assertIs(g0, g1)
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*/
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}
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[Ignore("Todo: Port")]
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[TestMethod]
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public void testDefaultGraph()
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{
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/*
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def testDefaultGraph(self):
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orig = ops.get_default_graph()
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self._AssertDefault(orig)
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g0 = ops.Graph()
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self._AssertDefault(orig)
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context_manager_0 = g0.as_default()
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self._AssertDefault(orig)
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with context_manager_0 as g0:
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self._AssertDefault(g0)
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with ops.Graph().as_default() as g1:
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self._AssertDefault(g1)
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self._AssertDefault(g0)
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self._AssertDefault(orig)
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*/
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}
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[Ignore("Todo: Port")]
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[TestMethod]
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public void testPreventFeeding()
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{
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/*
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def testPreventFeeding(self):
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g = ops.Graph()
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a = constant_op.constant(2.0)
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self.assertTrue(g.is_feedable(a))
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g.prevent_feeding(a)
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self.assertFalse(g.is_feedable(a))
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*/
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}
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[Ignore("Todo: Port")]
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[TestMethod]
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public void testAsGraphElementConversions()
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{
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/*
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def testAsGraphElementConversions(self):
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class ConvertibleObj(object):
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def _as_graph_element(self):
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return "FloatOutput:0"
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class NonConvertibleObj(object):
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pass
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g = ops.Graph()
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a = _apply_op(g, "FloatOutput", [], [dtypes.float32])
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self.assertEqual(a, g.as_graph_element(ConvertibleObj()))
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with self.assertRaises(TypeError):
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g.as_graph_element(NonConvertibleObj())
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*/
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}
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[Ignore("Todo: Port")]
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[TestMethod]
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public void testGarbageCollected()
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{
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/*
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# Regression test against creating custom __del__ functions in classes
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# involved in cyclic references, e.g. Graph and Operation. (Python won't gc
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# cycles that require calling a __del__ method, because the __del__ method can
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# theoretically increase the object's refcount to "save" it from gc, and any
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# already-deleted objects in the cycle would have be to restored.)
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def testGarbageCollected(self):
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# Create a graph we can delete and a weak reference to monitor if it's gc'd
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g = ops.Graph()
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g_ref = weakref.ref(g)
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# Create some ops
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with g.as_default():
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a = constant_op.constant(2.0)
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b = constant_op.constant(3.0)
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c = math_ops.add(a, b)
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# Create a session we can delete
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with session.Session(graph=g) as sess:
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self.evaluate(c)
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# Delete all references and trigger gc
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del g
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del a
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del b
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del c
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del sess
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gc.collect()
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self.assertIsNone(g_ref())
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*/
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}
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[Ignore("Todo: Port")]
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[TestMethod]
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public void testRunnableAfterInvalidShape()
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{
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/*
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def testRunnableAfterInvalidShape(self):
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with ops.Graph().as_default():
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with self.assertRaises(ValueError):
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math_ops.add([1, 2], [1, 2, 3])
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a = constant_op.constant(1)
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with session.Session() as sess:
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self.evaluate(a)
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*/
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}
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[Ignore("Todo: Port")]
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[TestMethod]
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public void testRunnableAfterInvalidShapeWithKernelLabelMap()
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{
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/*
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def testRunnableAfterInvalidShapeWithKernelLabelMap(self):
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g = ops.Graph()
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with g.as_default():
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with g._kernel_label_map({"KernelLabelRequired": "overload_1"}):
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with self.assertRaises(ValueError):
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test_ops.kernel_label_required(1)
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a = constant_op.constant(1)
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with session.Session() as sess:
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self.evaluate(a)
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*/
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}
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}
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}

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