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using Microsoft.VisualStudio.TestTools.UnitTesting;
using NumSharp;
using System;
using System.Linq;
using System.Runtime.InteropServices;
using System.Threading;
using FluentAssertions;
using Tensorflow;
using static Tensorflow.Binding;
using Tensorflow.Framework;
namespace TensorFlowNET.UnitTest
{
[TestClass]
public class TensorTest : CApiTest
{
[TestMethod]
public unsafe void TensorFromFixed()
{
var array = new float[1000];
var span = new Span<float>(array, 100, 500);
fixed (float* ptr = &MemoryMarshal.GetReference(span))
{
using (var t = new Tensor((IntPtr) ptr, new long[] {span.Length}, tf.float32, 4 * span.Length))
{
Assert.IsFalse(t.IsDisposed);
Assert.AreEqual(2000, (int) t.bytesize);
}
}
fixed (float* ptr = &array[0])
{
using (var t = new Tensor((IntPtr) ptr, new long[] {array.Length}, tf.float32, 4 * array.Length))
{
Assert.IsFalse(t.IsDisposed);
Assert.AreEqual(4000, (int) t.bytesize);
}
}
}
[TestMethod]
public unsafe void TensorFromArray()
{
var array = new float[1000];
using (var t = new Tensor(array, new long[] {array.Length}, tf.float32))
{
Assert.IsFalse(t.IsDisposed);
Assert.AreEqual(1000 * sizeof(float), (int) t.bytesize);
}
using (var t = new Tensor(new float[] {1}, new long[] {1}, tf.float32))
{
Assert.IsFalse(t.IsDisposed);
Assert.AreEqual(1 * sizeof(float), (int) t.bytesize);
}
using (var t = new Tensor(new float[] {1}, null, tf.float32))
{
Assert.IsFalse(t.IsDisposed);
Assert.AreEqual(1 * sizeof(float), (int) t.bytesize);
t.shape.Should().BeEmpty();
}
}
[TestMethod]
public void AllocateTensor()
{
ulong num_bytes = 6 * sizeof(float);
long[] dims = {2, 3};
Tensor t = c_api.TF_AllocateTensor(TF_DataType.TF_FLOAT, dims, 2, num_bytes);
EXPECT_EQ(TF_DataType.TF_FLOAT, t.dtype);
EXPECT_EQ(2, t.NDims);
EXPECT_EQ((int) dims[0], t.shape[0]);
EXPECT_EQ(num_bytes, t.bytesize);
t.Dispose();
}
/// <summary>
/// Port from c_api_test.cc
/// `TEST(CAPI, MaybeMove)`
/// </summary>
[TestMethod]
public void MaybeMove()
{
NDArray nd = np.array(2, 3);
Tensor t = new Tensor(nd);
Tensor o = t.MaybeMove();
ASSERT_TRUE(o == IntPtr.Zero); // It is unsafe to move memory TF might not own.
t.Dispose();
}
/// <summary>
/// Port from c_api_test.cc
/// `TEST(CAPI, Tensor)`
/// </summary>
[TestMethod]
public void Tensor()
{
var nd = np.array(1f, 2f, 3f, 4f, 5f, 6f).reshape(2, 3);
var tensor = new Tensor(nd);
var array = tensor.ToArray<float>();
EXPECT_EQ(tensor.dtype, TF_DataType.TF_FLOAT);
EXPECT_EQ(tensor.rank, nd.ndim);
EXPECT_EQ((int) tensor.shape[0], nd.shape[0]);
EXPECT_EQ((int) tensor.shape[1], nd.shape[1]);
EXPECT_EQ(tensor.bytesize, (ulong) nd.size * sizeof(float));
Assert.IsTrue(Enumerable.SequenceEqual(nd.Data<float>(), new float[] {1, 2, 3, 4, 5, 6}));
}
/// <summary>
/// Port from tensorflow\c\c_api_test.cc
/// `TEST(CAPI, SetShape)`
/// </summary>
[TestMethod]
public void SetShape()
{
var s = new Status();
var graph = new Graph().as_default();
var feed = c_test_util.Placeholder(graph, s);
var feed_out_0 = new TF_Output(feed, 0);
// Fetch the shape, it should be completely unknown.
int num_dims = c_api.TF_GraphGetTensorNumDims(graph, feed_out_0, s);
Assert.IsTrue(s.Code == TF_Code.TF_OK);
EXPECT_EQ(-1, num_dims);
// Set the shape to be unknown, expect no change.
c_api.TF_GraphSetTensorShape(graph, feed_out_0, null, -1, s);
Assert.IsTrue(s.Code == TF_Code.TF_OK);
num_dims = c_api.TF_GraphGetTensorNumDims(graph, feed_out_0, s);
EXPECT_EQ(-1, num_dims);
// Set the shape to be 2 x Unknown
long[] dims = {2, -1};
c_api.TF_GraphSetTensorShape(graph, feed_out_0, dims, dims.Length, s);
Assert.IsTrue(s.Code == TF_Code.TF_OK);
num_dims = c_api.TF_GraphGetTensorNumDims(graph, feed_out_0, s);
EXPECT_EQ(2, num_dims);
// Get the dimension vector appropriately.
var returned_dims = new long[dims.Length];
c_api.TF_GraphGetTensorShape(graph, feed_out_0, returned_dims, num_dims, s);
Assert.IsTrue(s.Code == TF_Code.TF_OK);
Assert.IsTrue(Enumerable.SequenceEqual(dims, returned_dims));
// Set to a new valid shape: [2, 3]
dims[1] = 3;
c_api.TF_GraphSetTensorShape(graph, feed_out_0, dims, dims.Length, s);
Assert.IsTrue(s.Code == TF_Code.TF_OK);
// Fetch and see that the new value is returned.
c_api.TF_GraphGetTensorShape(graph, feed_out_0, returned_dims, num_dims, s);
Assert.IsTrue(s.Code == TF_Code.TF_OK);
Assert.IsTrue(Enumerable.SequenceEqual(dims, returned_dims));
// Try to set 'unknown' with unknown rank on the shape and see that
// it doesn't change.
c_api.TF_GraphSetTensorShape(graph, feed_out_0, null, -1, s);
Assert.IsTrue(s.Code == TF_Code.TF_OK);
c_api.TF_GraphGetTensorShape(graph, feed_out_0, returned_dims, num_dims, s);
Assert.IsTrue(s.Code == TF_Code.TF_OK);
EXPECT_EQ(2, num_dims);
EXPECT_EQ(2, (int) returned_dims[0]);
EXPECT_EQ(3, (int) returned_dims[1]);
// Try to set 'unknown' with same rank on the shape and see that
// it doesn't change.
dims[0] = -1;
dims[1] = -1;
c_api.TF_GraphSetTensorShape(graph, feed_out_0, dims, 2, s);
Assert.IsTrue(s.Code == TF_Code.TF_OK);
c_api.TF_GraphGetTensorShape(graph, feed_out_0, returned_dims, num_dims, s);
Assert.IsTrue(s.Code == TF_Code.TF_OK);
EXPECT_EQ(2, num_dims);
EXPECT_EQ(2, (int) returned_dims[0]);
EXPECT_EQ(3, (int) returned_dims[1]);
// Try to fetch a shape with the wrong num_dims
c_api.TF_GraphGetTensorShape(graph, feed_out_0, returned_dims, 5, s);
Assert.IsTrue(s.Code == TF_Code.TF_INVALID_ARGUMENT);
// Try to set an invalid shape (cannot change 2x3 to a 2x5).
dims[1] = 5;
c_api.TF_GraphSetTensorShape(graph, feed_out_0, dims, 2, s);
Assert.IsTrue(s.Code == TF_Code.TF_INVALID_ARGUMENT);
// Test for a scalar.
var three = c_test_util.ScalarConst(3, graph, s);
Assert.IsTrue(s.Code == TF_Code.TF_OK);
var three_out_0 = new TF_Output(three, 0);
num_dims = c_api.TF_GraphGetTensorNumDims(graph, three_out_0, s);
Assert.IsTrue(s.Code == TF_Code.TF_OK);
EXPECT_EQ(0, num_dims);
c_api.TF_GraphGetTensorShape(graph, feed_out_0, null, num_dims, s);
//Assert.IsTrue(s.Code == TF_Code.TF_OK);
// graph.Dispose();
s.Dispose();
}
[TestMethod]
public void sparse_to_dense()
{
var indices = tf.reshape(tf.range(0, 5), new int[] { 5, 1 });
var labels = tf.expand_dims(tf.constant(new[] { 0, 1, 2, 3, 4 }),1);
var st = tf.concat(values: new[] { indices, labels }, axis: 1);
var onehot = tf.sparse_to_dense(st, (5, 5), 1);
using (var sess = tf.Session())
{
var result = sess.run(onehot);
Assert.IsTrue(Enumerable.SequenceEqual(new int[] { 1, 0, 0, 0, 0 }, result[0].ToArray<int>()));
Assert.IsTrue(Enumerable.SequenceEqual(new int[] { 0, 1, 0, 0, 0 }, result[1].ToArray<int>()));
Assert.IsTrue(Enumerable.SequenceEqual(new int[] { 0, 0, 1, 0, 0 }, result[2].ToArray<int>()));
Assert.IsTrue(Enumerable.SequenceEqual(new int[] { 0, 0, 0, 1, 0 }, result[3].ToArray<int>()));
Assert.IsTrue(Enumerable.SequenceEqual(new int[] { 0, 0, 0, 0, 1 }, result[4].ToArray<int>()));
};
}
[TestMethod]
public void sparse_tensor_to_dense()
{
var decoded_list = tf.SparseTensor(new[,]
{
{ 0L, 0L },
{ 1L, 2L }
},
new int[] { 1, 2 },
new[] { 3L, 4L });
var onehot = tf.sparse_tensor_to_dense(decoded_list);
using (var sess = tf.Session())
{
var result = sess.run(onehot);
Assert.IsTrue(Enumerable.SequenceEqual(new int[] { 1, 0, 0, 0 }, result[0].ToArray<int>()));
Assert.IsTrue(Enumerable.SequenceEqual(new int[] { 0, 0, 2, 0 }, result[1].ToArray<int>()));
Assert.IsTrue(Enumerable.SequenceEqual(new int[] { 0, 0, 0, 0 }, result[2].ToArray<int>()));
}
}
[TestMethod]
public void batch_to_space_nd()
{
var inputs = np.arange(24).reshape(4, 2, 3);
var block_shape = new[] { 2, 2 };
int[,] crops = { { 0, 0 }, { 0, 0 } };
var tensor = tf.batch_to_space_nd(inputs, block_shape, crops);
using (var sess = tf.Session())
{
var result = sess.run(tensor);
Assert.IsTrue(Enumerable.SequenceEqual(new int[] { 0, 6, 1, 7, 2, 8 }, result[0, 0].ToArray<int>()));
Assert.IsTrue(Enumerable.SequenceEqual(new int[] { 12, 18, 13, 19, 14, 20 }, result[0, 1].ToArray<int>()));
Assert.IsTrue(Enumerable.SequenceEqual(new int[] { 3, 9, 4, 10, 5, 11 }, result[0, 2].ToArray<int>()));
Assert.IsTrue(Enumerable.SequenceEqual(new int[] { 15, 21, 16, 22, 17, 23 }, result[0, 3].ToArray<int>()));
}
}
[TestMethod]
public void boolean_mask()
{
var tensor = new[] { 0, 1, 2, 3 };
var mask = np.array(new[] { true, false, true, false });
var masked = tf.boolean_mask(tensor, mask);
using (var sess = tf.Session())
{
var result = sess.run(masked);
Assert.IsTrue(Enumerable.SequenceEqual(new int[] { 0, 2 }, result.ToArray<int>()));
}
}
}
}