TensorFlow.NET/test/TensorFlowNET.UnitTest/PythonTest.cs at master · phikyzom/TensorFlow.NET · GitHub

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using System;

using System.Collections;

using System.Collections.Generic;

using System.Linq;

using System.Text;

using Microsoft.VisualStudio.TestTools.UnitTesting;

using Newtonsoft.Json.Linq;

using NumSharp;

using Tensorflow;

using Tensorflow.Util;

namespace TensorFlowNET.UnitTest

{

/// <summary>

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

/// </summary>

public class PythonTest : Python

{

#region python compatibility layer

protected PythonTest self { get => this; }

protected object None

{

get { return null; }

}

#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).Array, (e[i] as NDArray).Array);

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 assertEqual(object given, object expected)

{

if (given is NDArray && expected is NDArray)

{

assertItemsEqual((given as NDArray).Array, (expected as NDArray).Array);

return;

}

if (given is Hashtable && expected is Hashtable)

{

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

return;

}

if (given is ICollection && expected is ICollection)

{

assertItemsEqual(given as ICollection, expected as ICollection);

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)

{

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

}

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

{

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

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

}

public void assertProtoEquals(object toProto, object o)

{

throw new NotImplementedException();

}

#endregion

#region tensor evaluation and test session

protected object _eval_helper(Tensor[] tensors)

{

if (tensors == null)

return null;

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

return null;

}

protected object _eval_tensor(object tensor)

{

if (tensor == None)

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 e)

{

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:

{

with(tf.Session(), s =>

{

var ndarray=tensor.eval();

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

{

double x = ndarray;

result=x;

}

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

{

int x = ndarray;

result = x;

}

else

{

result = ndarray;

}

});

return (T)result;

}

public Session cached_session()

{

throw new NotImplementedException();

}

//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);

self._constrain_devices_and_set_default(s, use_gpu, force_gpu);

//}

return s.as_default();

}

private IPython _constrain_devices_and_set_default(Session sess, bool useGpu, bool forceGpu)

{

//def _constrain_devices_and_set_default(self, sess, use_gpu, force_gpu):

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

//if context.executing_eagerly():

// yield None

//else:

// with sess.graph.as_default(), sess.as_default():

// if force_gpu:

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

// # '/device:GPU:0' otherwise

// gpu_name = gpu_device_name()

// if not gpu_name:

// gpu_name = "/device:GPU:0"

// with sess.graph.device(gpu_name):

// yield sess

// elif use_gpu:

// yield sess

// else:

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

// yield sess

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: graph);//, config = prepare_config(config))

}

#endregion

}

TensorFlow.NET/test/TensorFlowNET.UnitTest/PythonTest.cs at master · phikyzom/TensorFlow.NET · GitHub

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