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Added: fft, ifft, fft2d, ifft2d, fft3d, ifft3d #1016

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Apr 2, 2023
Merged

Added: fft, ifft, fft2d, ifft2d, fft3d, ifft3d #1016

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40 changes: 40 additions & 0 deletions src/TensorFlowNET.Core/APIs/tf.signal.cs
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@@ -0,0 +1,40 @@
/*****************************************************************************
Copyright 2023 Konstantin Balashov All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
******************************************************************************/

using Tensorflow.Operations;

namespace Tensorflow
{
public partial class tensorflow
{
public SignalApi signal { get; } = new SignalApi();
public class SignalApi
{
public Tensor fft(Tensor input, string name = null)
=> gen_ops.f_f_t(input, name: name);
public Tensor ifft(Tensor input, string name = null)
=> gen_ops.i_f_f_t(input, name: name);
public Tensor fft2d(Tensor input, string name = null)
=> gen_ops.f_f_t2d(input, name: name);
public Tensor ifft2d(Tensor input, string name = null)
=> gen_ops.i_f_f_t2d(input, name: name);
public Tensor fft3d(Tensor input, string name = null)
=> gen_ops.f_f_t3d(input, name: name);
public Tensor ifft3d(Tensor input, string name = null)
=> gen_ops.i_f_f_t3d(input, name: name);
}
}
}
30 changes: 6 additions & 24 deletions src/TensorFlowNET.Core/Operations/gen_ops.cs
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Expand Up @@ -10475,10 +10475,7 @@ public static Tensor extract_jpeg_shape(Tensor contents, TF_DataType? output_typ
/// </remarks>
public static Tensor f_f_t(Tensor input, string name = "FFT")
{
var dict = new Dictionary<string, object>();
dict["input"] = input;
var op = tf.OpDefLib._apply_op_helper("FFT", name: name, keywords: dict);
return op.output;
return tf.Context.ExecuteOp("FFT", name, new ExecuteOpArgs(input));
}

/// <summary>
Expand All @@ -10505,10 +10502,7 @@ public static Tensor f_f_t(Tensor input, string name = "FFT")
/// </remarks>
public static Tensor f_f_t2d(Tensor input, string name = "FFT2D")
{
var dict = new Dictionary<string, object>();
dict["input"] = input;
var op = tf.OpDefLib._apply_op_helper("FFT2D", name: name, keywords: dict);
return op.output;
return tf.Context.ExecuteOp("FFT2D", name, new ExecuteOpArgs(input));
}

/// <summary>
Expand All @@ -10535,10 +10529,7 @@ public static Tensor f_f_t2d(Tensor input, string name = "FFT2D")
/// </remarks>
public static Tensor f_f_t3d(Tensor input, string name = "FFT3D")
{
var dict = new Dictionary<string, object>();
dict["input"] = input;
var op = tf.OpDefLib._apply_op_helper("FFT3D", name: name, keywords: dict);
return op.output;
return tf.Context.ExecuteOp("FFT3D", name, new ExecuteOpArgs(input));
}

/// <summary>
Expand Down Expand Up @@ -12861,10 +12852,7 @@ public static Tensor host_const(Tensor value, TF_DataType dtype, string name = "
/// </remarks>
public static Tensor i_f_f_t(Tensor input, string name = "IFFT")
{
var dict = new Dictionary<string, object>();
dict["input"] = input;
var op = tf.OpDefLib._apply_op_helper("IFFT", name: name, keywords: dict);
return op.output;
return tf.Context.ExecuteOp("IFFT", name, new ExecuteOpArgs(input));
}

/// <summary>
Expand All @@ -12891,10 +12879,7 @@ public static Tensor i_f_f_t(Tensor input, string name = "IFFT")
/// </remarks>
public static Tensor i_f_f_t2d(Tensor input, string name = "IFFT2D")
{
var dict = new Dictionary<string, object>();
dict["input"] = input;
var op = tf.OpDefLib._apply_op_helper("IFFT2D", name: name, keywords: dict);
return op.output;
return tf.Context.ExecuteOp("IFFT2D", name, new ExecuteOpArgs(input));
}

/// <summary>
Expand All @@ -12921,10 +12906,7 @@ public static Tensor i_f_f_t2d(Tensor input, string name = "IFFT2D")
/// </remarks>
public static Tensor i_f_f_t3d(Tensor input, string name = "IFFT3D")
{
var dict = new Dictionary<string, object>();
dict["input"] = input;
var op = tf.OpDefLib._apply_op_helper("IFFT3D", name: name, keywords: dict);
return op.output;
return tf.Context.ExecuteOp("IFFT3D", name, new ExecuteOpArgs(input));
}

/// <summary>
Expand Down
103 changes: 103 additions & 0 deletions test/TensorFlowNET.Graph.UnitTest/SignalTest.cs
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using Microsoft.VisualStudio.TestTools.UnitTesting;
using Tensorflow.NumPy;
using System;
using System.Collections.Generic;
using System.Linq;
using Tensorflow;
using static Tensorflow.Binding;
using Buffer = Tensorflow.Buffer;
using TensorFlowNET.Keras.UnitTest;

namespace TensorFlowNET.UnitTest.Basics
{
[TestClass]
public class SignalTest : EagerModeTestBase
{
[TestMethod]
public void fft()
{
double[] d_real = new double[] { 1.0, 2.0, 3.0, 4.0 };
double[] d_imag = new double[] { -1.0, -3.0, 5.0, 7.0 };

Tensor t_real = tf.constant(d_real, dtype: TF_DataType.TF_DOUBLE);
Tensor t_imag = tf.constant(d_imag, dtype: TF_DataType.TF_DOUBLE);

Tensor t_complex = tf.complex(t_real, t_imag);

Tensor t_frequency_domain = tf.signal.fft(t_complex);
Tensor f_time_domain = tf.signal.ifft(t_frequency_domain);

Tensor t_real_result = tf.math.real(f_time_domain);
Tensor t_imag_result = tf.math.imag(f_time_domain);

NDArray n_real_result = t_real_result.numpy();
NDArray n_imag_result = t_imag_result.numpy();

double[] d_real_result = n_real_result.ToArray<double>();
double[] d_imag_result = n_imag_result.ToArray<double>();

Assert.IsTrue(base.Equal(d_real_result, d_real));
Assert.IsTrue(base.Equal(d_imag_result, d_imag));
}
[TestMethod]
public void fft2d()
{
double[] d_real = new double[] { 1.0, 2.0, 3.0, 4.0 };
double[] d_imag = new double[] { -1.0, -3.0, 5.0, 7.0 };

Tensor t_real = tf.constant(d_real, dtype: TF_DataType.TF_DOUBLE);
Tensor t_imag = tf.constant(d_imag, dtype: TF_DataType.TF_DOUBLE);

Tensor t_complex = tf.complex(t_real, t_imag);

Tensor t_complex_2d = tf.reshape(t_complex,new int[] { 2, 2 });

Tensor t_frequency_domain_2d = tf.signal.fft2d(t_complex_2d);
Tensor t_time_domain_2d = tf.signal.ifft2d(t_frequency_domain_2d);

Tensor t_time_domain = tf.reshape(t_time_domain_2d, new int[] { 4 });

Tensor t_real_result = tf.math.real(t_time_domain);
Tensor t_imag_result = tf.math.imag(t_time_domain);

NDArray n_real_result = t_real_result.numpy();
NDArray n_imag_result = t_imag_result.numpy();

double[] d_real_result = n_real_result.ToArray<double>();
double[] d_imag_result = n_imag_result.ToArray<double>();

Assert.IsTrue(base.Equal(d_real_result, d_real));
Assert.IsTrue(base.Equal(d_imag_result, d_imag));
}
[TestMethod]
public void fft3d()
{
double[] d_real = new double[] { 1.0, 2.0, 3.0, 4.0, -3.0, -2.0, -1.0, -4.0 };
double[] d_imag = new double[] { -1.0, -3.0, 5.0, 7.0, 6.0, 4.0, 2.0, 0.0};

Tensor t_real = tf.constant(d_real, dtype: TF_DataType.TF_DOUBLE);
Tensor t_imag = tf.constant(d_imag, dtype: TF_DataType.TF_DOUBLE);

Tensor t_complex = tf.complex(t_real, t_imag);

Tensor t_complex_3d = tf.reshape(t_complex, new int[] { 2, 2, 2 });

Tensor t_frequency_domain_3d = tf.signal.fft2d(t_complex_3d);
Tensor t_time_domain_3d = tf.signal.ifft2d(t_frequency_domain_3d);

Tensor t_time_domain = tf.reshape(t_time_domain_3d, new int[] { 8 });

Tensor t_real_result = tf.math.real(t_time_domain);
Tensor t_imag_result = tf.math.imag(t_time_domain);

NDArray n_real_result = t_real_result.numpy();
NDArray n_imag_result = t_imag_result.numpy();

double[] d_real_result = n_real_result.ToArray<double>();
double[] d_imag_result = n_imag_result.ToArray<double>();

Assert.IsTrue(base.Equal(d_real_result, d_real));
Assert.IsTrue(base.Equal(d_imag_result, d_imag));
}
}
}