TensorFlow.NET/test/Tensorflow.UnitTest/PythonTest.cs at master · ehtick/TensorFlow.NET · GitHub

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using Microsoft.VisualStudio.TestTools.UnitTesting;

using Newtonsoft.Json.Linq;

using Tensorflow.NumPy;

using System.Collections;

using Tensorflow;

using static Tensorflow.Binding;

namespace TensorFlowNET.UnitTest

{

/// <summary>

/// Use as base class for test classes to get additional assertions

/// </summary>

public class PythonTest

{

#region python compatibility layer

protected PythonTest self { get => this; }

protected int None => -1;

#endregion

#region pytest assertions

public void assertItemsEqual(ICollection given, ICollection expected)

{

if (given is Hashtable && expected is Hashtable)

{

Assert.AreEqual(JObject.FromObject(expected).ToString(), JObject.FromObject(given).ToString());

return;

}

Assert.IsNotNull(expected);

Assert.IsNotNull(given);

var e = expected.OfType<object>().ToArray();

var g = given.OfType<object>().ToArray();

Assert.AreEqual(e.Length, g.Length, $"The collections differ in length expected {e.Length} but got {g.Length}");

for (int i = 0; i < e.Length; i++)

{

/*if (g[i] is NDArray && e[i] is NDArray)

assertItemsEqual((g[i] as NDArray).GetData<object>(), (e[i] as NDArray).GetData<object>());

else*/

if (e[i] is ICollection && g[i] is ICollection)

assertEqual(g[i], e[i]);

else

Assert.AreEqual(e[i], g[i], $"Items differ at index {i}, expected {e[i]} but got {g[i]}");

}

public void assertAllEqual(ICollection given, ICollection expected)

{

assertItemsEqual(given, expected);

}

public void assertFloat32Equal(float expected, float actual, string msg)

{

float eps = 1e-6f;

Assert.IsTrue(Math.Abs(expected - actual) < eps * Math.Max(1.0f, Math.Abs(expected)), $"{msg}: expected {expected} vs actual {actual}");

}

public void assertFloat64Equal(double expected, double actual, string msg)

{

double eps = 1e-16f;

Assert.IsTrue(Math.Abs(expected - actual) < eps * Math.Max(1.0f, Math.Abs(expected)), $"{msg}: expected {expected} vs actual {actual}");

}

public void AssetSequenceEqual(float[] expected, float[] actual)

{

float eps = 1e-5f;

for (int i = 0; i < expected.Length; i++)

Assert.IsTrue(Math.Abs(expected[i] - actual[i]) < eps * Math.Max(1.0f, Math.Abs(expected[i])), $"expected {expected} vs actual {actual}");

}

public void AssetSequenceEqual(double[] expected, double[] actual)

{

double eps = 1e-5f;

for (int i = 0; i < expected.Length; i++)

Assert.IsTrue(Math.Abs(expected[i] - actual[i]) < eps * Math.Max(1.0f, Math.Abs(expected[i])), $"expected {expected} vs actual {actual}");

}

public void assertEqual(object given, object expected)

{

/*if (given is NDArray && expected is NDArray)

{

assertItemsEqual((given as NDArray).GetData<object>(), (expected as NDArray).GetData<object>());

return;

}*/

if (given is Hashtable && expected is Hashtable)

{

Assert.AreEqual(JObject.FromObject(expected).ToString(), JObject.FromObject(given).ToString());

return;

}

if (given is ICollection collectionGiven && expected is ICollection collectionExpected)

{

assertItemsEqual(collectionGiven, collectionExpected);

return;

}

if (given is float && expected is float)

{

assertFloat32Equal((float)expected, (float)given, "");

return;

}

if (given is double && expected is double)

{

assertFloat64Equal((double)expected, (double)given, "");

return;

}

Assert.AreEqual(expected, given);

}

public void assertEquals(object given, object expected)

{

assertEqual(given, expected);

}

public void assert(object given)

{

if (given is bool)

Assert.IsTrue((bool)given);

Assert.IsNotNull(given);

}

public void assertIsNotNone(object given)

{

Assert.IsNotNull(given);

}

public void assertFalse(bool cond)

{

Assert.IsFalse(cond);

}

public void assertTrue(bool cond)

{

Assert.IsTrue(cond);

}

public void assertAllClose(NDArray array1, NDArray array2, double eps = 1e-5)

{

CollectionAssert.AreEqual(array1.ToArray(), array2.ToArray(), new CollectionComparer(eps));

//TODO: Assert.IsTrue(np.allclose(array1, array2, rtol: eps));

}

public void assertAllClose(double value, NDArray array2, double eps = 1e-5)

{

if (array2.shape.IsScalar)

{

double value2 = array2;

Assert.AreEqual(value, value2, eps);

return;

}

var array1 = np.ones_like(array2) * value;

CollectionAssert.AreEqual(array1.ToArray(), array2.ToArray(), new CollectionComparer(eps));

//TODO: Assert.IsTrue(np.allclose(array1, array2, rtol: eps));

}

private class CollectionComparer : IComparer

{

private readonly double _epsilon;

public CollectionComparer(double eps = 1e-06)

{

_epsilon = eps;

}

public int Compare(object? x, object? y)

{

if (x == null && y == null)

{

return 0;

}

else if (x == null)

{

return -1;

}

else if (y == null)

{

return 1;

}

var a = Convert.ToDouble(x);

var b = Convert.ToDouble(y);

double delta = Math.Abs(a - b);

if (delta < _epsilon)

{

return 0;

}

return a.CompareTo(b);

}

public void assertAllCloseAccordingToType<T>(

double[,] expected,

T[,] given,

double eps = 1e-6,

float float_eps = 1e-6f)

{

Assert.AreEqual(expected.GetLength(0), given.GetLength(0));

Assert.AreEqual(expected.GetLength(1), given.GetLength(1));

var flattenGiven = given.Cast<T>().ToArray();

assertAllCloseAccordingToType(expected, flattenGiven, eps, float_eps);

}

public void assertAllCloseAccordingToType<T>(

ICollection expected,

ICollection<T> given,

double eps = 1e-6,

float float_eps = 1e-6f)

{

// TODO: check if any of arguments is not double and change toletance

// remove givenAsDouble and cast expected instead

var givenAsDouble = given.Select(x => Convert.ToDouble(x)).ToArray();

CollectionAssert.AreEqual(expected, givenAsDouble, new CollectionComparer(eps));

}

public void assertProtoEquals(object toProto, object o)

{

throw new NotImplementedException();

}

#endregion

#region tensor evaluation and test session

private Session? _cached_session = null;

private Graph? _cached_graph = null;

private object? _cached_config = null;

private bool _cached_force_gpu = false;

private void _ClearCachedSession()

{

if (self._cached_session != null)

{

self._cached_session.Dispose();

self._cached_session = null;

}

//protected object _eval_helper(Tensor[] tensors)

//{

// if (tensors == null)

// return null;

// return nest.map_structure(self._eval_tensor, tensors);

//}

protected object? _eval_tensor(object tensor)

{

if (tensor == null)

return None;

//else if (callable(tensor))

// return self._eval_helper(tensor())

else

{

try

{

//TODO:

// if sparse_tensor.is_sparse(tensor):

// return sparse_tensor.SparseTensorValue(tensor.indices, tensor.values,

// tensor.dense_shape)

//return (tensor as Tensor).numpy();

}

catch (Exception)

{

throw new ValueError("Unsupported type: " + tensor.GetType());

}

return null;

}

/// <summary>

/// This function is used in many original tensorflow unit tests to evaluate tensors

/// in a test session with special settings (for instance constant folding off)

///

/// </summary>

public T evaluate<T>(Tensor tensor)

{

object? result = null;

// if context.executing_eagerly():

// return self._eval_helper(tensors)

// else:

{

var sess = tf.get_default_session();

var ndarray = tensor.eval(sess);

if (typeof(T) == typeof(int))

{

int i = ndarray;

result = i;

}

else if (typeof(T) == typeof(float))

{

float f = ndarray;

result = f;

}

else if (typeof(T) == typeof(double))

{

double d = ndarray;

result = d;

}

else if (

typeof(T) == typeof(double[])

|| typeof(T) == typeof(double[,]))

{

result = ndarray.ToMultiDimArray<double>();

}

else if (typeof(T) == typeof(float[])

|| typeof(T) == typeof(float[,]))

{

result = ndarray.ToMultiDimArray<float>();

}

else if (typeof(T) == typeof(int[])

|| typeof(T) == typeof(int[,]))

{

result = ndarray.ToMultiDimArray<int>();

}

else

{

result = ndarray;

}

return (T)result;

}

///Returns a TensorFlow Session for use in executing tests.

public Session? cached_session(

Graph? graph = null, object? config = null, bool use_gpu = false, bool force_gpu = false)

{

// This method behaves differently than self.session(): for performance reasons

// `cached_session` will by default reuse the same session within the same

// test.The session returned by this function will only be closed at the end

// of the test(in the TearDown function).

// Use the `use_gpu` and `force_gpu` options to control where ops are run.If

// `force_gpu` is True, all ops are pinned to `/ device:GPU:0`. Otherwise, if

// `use_gpu` is True, TensorFlow tries to run as many ops on the GPU as

// possible.If both `force_gpu and `use_gpu` are False, all ops are pinned to

// the CPU.

// Example:

// python

// class MyOperatorTest(test_util.TensorFlowTestCase) :

// def testMyOperator(self):

// with self.cached_session() as sess:

// valid_input = [1.0, 2.0, 3.0, 4.0, 5.0]

// result = MyOperator(valid_input).eval()

// self.assertEqual(result, [1.0, 2.0, 3.0, 5.0, 8.0]

// invalid_input = [-1.0, 2.0, 7.0]

// with self.assertRaisesOpError("negative input not supported"):

// MyOperator(invalid_input).eval()

// Args:

// graph: Optional graph to use during the returned session.

// config: An optional config_pb2.ConfigProto to use to configure the

// session.

// use_gpu: If True, attempt to run as many ops as possible on GPU.

// force_gpu: If True, pin all ops to `/device:GPU:0`.

// Yields:

// A Session object that should be used as a context manager to surround

// the graph building and execution code in a test case.

// TODO:

// if context.executing_eagerly():

// return self._eval_helper(tensors)

// else:

{

var sess = self._get_cached_session(

graph, config, force_gpu, crash_if_inconsistent_args: true);

using var cached = self._constrain_devices_and_set_default(sess, use_gpu, force_gpu);

return cached;

}

//Returns a TensorFlow Session for use in executing tests.

public Session session(Graph? graph = null, object? config = null, bool use_gpu = false, bool force_gpu = false)

{

//Note that this will set this session and the graph as global defaults.

//Use the `use_gpu` and `force_gpu` options to control where ops are run.If

//`force_gpu` is True, all ops are pinned to `/device:GPU:0`. Otherwise, if

//`use_gpu` is True, TensorFlow tries to run as many ops on the GPU as

//possible.If both `force_gpu and `use_gpu` are False, all ops are pinned to

//the CPU.

//Example:

//```python

//class MyOperatorTest(test_util.TensorFlowTestCase):

// def testMyOperator(self):

// with self.session(use_gpu= True):

// valid_input = [1.0, 2.0, 3.0, 4.0, 5.0]

// result = MyOperator(valid_input).eval()

// self.assertEqual(result, [1.0, 2.0, 3.0, 5.0, 8.0]

// invalid_input = [-1.0, 2.0, 7.0]

// with self.assertRaisesOpError("negative input not supported"):

// MyOperator(invalid_input).eval()

//```

//Args:

// graph: Optional graph to use during the returned session.

// config: An optional config_pb2.ConfigProto to use to configure the

// session.

// use_gpu: If True, attempt to run as many ops as possible on GPU.

// force_gpu: If True, pin all ops to `/device:GPU:0`.

//Yields:

// A Session object that should be used as a context manager to surround

// the graph building and execution code in a test case.

Session? s = null;

//if (context.executing_eagerly())

// yield None

//else

//{

s = self._create_session(graph, config, force_gpu);

//}

return s.as_default();

}

private Session? _constrain_devices_and_set_default(Session sess, bool use_gpu, bool force_gpu)

{

// Set the session and its graph to global default and constrain devices."""

if (tf.executing_eagerly())

return null;

else

{

sess.graph.as_default();

sess.as_default();

{

if (force_gpu)

{

// TODO:

// Use the name of an actual device if one is detected, or

// '/device:GPU:0' otherwise

/* var gpu_name = gpu_device_name();

if (!gpu_name)

gpu_name = "/device:GPU:0"

using (sess.graph.device(gpu_name)) {

yield return sess;

}*/

return sess;

}

else if (use_gpu)

return sess;

else

using (sess.graph.device("/device:CPU:0"))

return sess;

}

// See session() for details.

private Session _create_session(Graph? graph, object? cfg, bool forceGpu)

{

var prepare_config = new Func<object, object>((config) =>

{

// """Returns a config for sessions.

// Args:

// config: An optional config_pb2.ConfigProto to use to configure the

// session.

// Returns:

// A config_pb2.ConfigProto object.

//TODO: config

// # use_gpu=False. Currently many tests rely on the fact that any device

// # will be used even when a specific device is supposed to be used.

// allow_soft_placement = not force_gpu

// if config is None:

// config = config_pb2.ConfigProto()

// config.allow_soft_placement = allow_soft_placement

// config.gpu_options.per_process_gpu_memory_fraction = 0.3

// elif not allow_soft_placement and config.allow_soft_placement:

// config_copy = config_pb2.ConfigProto()

// config_copy.CopyFrom(config)

// config = config_copy

// config.allow_soft_placement = False

// # Don't perform optimizations for tests so we don't inadvertently run

// # gpu ops on cpu

// config.graph_options.optimizer_options.opt_level = -1

// # Disable Grappler constant folding since some tests & benchmarks

// # use constant input and become meaningless after constant folding.

// # DO NOT DISABLE GRAPPLER OPTIMIZERS WITHOUT CONSULTING WITH THE

// # GRAPPLER TEAM.

// config.graph_options.rewrite_options.constant_folding = (

// rewriter_config_pb2.RewriterConfig.OFF)

// config.graph_options.rewrite_options.pin_to_host_optimization = (

// rewriter_config_pb2.RewriterConfig.OFF)

return config;

});

//TODO: use this instead of normal session

//return new ErrorLoggingSession(graph = graph, config = prepare_config(config))

return new Session(graph);//, config = prepare_config(config))

}

private Session _get_cached_session(

Graph? graph = null,

object? config = null,

bool force_gpu = false,

bool crash_if_inconsistent_args = true)

{

// See cached_session() for documentation.

if (self._cached_session == null)

{

var sess = self._create_session(graph, config, force_gpu);

self._cached_session = sess;

self._cached_graph = graph;

self._cached_config = config;

self._cached_force_gpu = force_gpu;

return sess;

}

else

{

if (crash_if_inconsistent_args && self._cached_graph != null && !self._cached_graph.Equals(graph))

throw new ValueError(@"The graph used to get the cached session is

different than the one that was used to create the

session. Maybe create a new session with

self.session()");

if (crash_if_inconsistent_args && self._cached_config != null && !self._cached_config.Equals(config))

{

throw new ValueError(@"The config used to get the cached session is

different than the one that was used to create the

session. Maybe create a new session with

self.session()");

}

if (crash_if_inconsistent_args && !self._cached_force_gpu.Equals(force_gpu))

{

throw new ValueError(@"The force_gpu value used to get the cached session is

different than the one that was used to create the

session. Maybe create a new session with

self.session()");

}

return self._cached_session;

}

[TestCleanup]

public void Cleanup()

{

_ClearCachedSession();

}

#endregion

public void AssetSequenceEqual<T>(T[] a, T[] b)

{

Assert.IsTrue(Enumerable.SequenceEqual(a, b));

}

TensorFlow.NET/test/Tensorflow.UnitTest/PythonTest.cs at master · ehtick/TensorFlow.NET · GitHub

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