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BalashovKBalashovK
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Added: fft, ifft, fft2d, ifft2d, fft3d, ifft3d
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-24
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3 files changed

+149
-24
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‎src/TensorFlowNET.Core/APIs/tf.signal.cs

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/*****************************************************************************
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Copyright 2023 Konstantin Balashov All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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******************************************************************************/
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using Tensorflow.Operations;
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namespace Tensorflow
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{
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public partial class tensorflow
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{
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public SignalApi signal { get; } = new SignalApi();
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public class SignalApi
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{
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public Tensor fft(Tensor input, string name = null)
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=> gen_ops.f_f_t(input, name: name);
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public Tensor ifft(Tensor input, string name = null)
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=> gen_ops.i_f_f_t(input, name: name);
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public Tensor fft2d(Tensor input, string name = null)
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=> gen_ops.f_f_t2d(input, name: name);
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public Tensor ifft2d(Tensor input, string name = null)
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=> gen_ops.i_f_f_t2d(input, name: name);
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public Tensor fft3d(Tensor input, string name = null)
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=> gen_ops.f_f_t3d(input, name: name);
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public Tensor ifft3d(Tensor input, string name = null)
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=> gen_ops.i_f_f_t3d(input, name: name);
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}
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}
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}

‎src/TensorFlowNET.Core/Operations/gen_ops.cs

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@@ -10475,10 +10475,7 @@ public static Tensor extract_jpeg_shape(Tensor contents, TF_DataType? output_typ
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/// </remarks>
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public static Tensor f_f_t(Tensor input, string name = "FFT")
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{
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var dict = new Dictionary<string, object>();
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dict["input"] = input;
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var op = tf.OpDefLib._apply_op_helper("FFT", name: name, keywords: dict);
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return op.output;
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return tf.Context.ExecuteOp("FFT", name, new ExecuteOpArgs(input));
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}
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/// <summary>
@@ -10505,10 +10502,7 @@ public static Tensor f_f_t(Tensor input, string name = "FFT")
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/// </remarks>
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public static Tensor f_f_t2d(Tensor input, string name = "FFT2D")
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{
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var dict = new Dictionary<string, object>();
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dict["input"] = input;
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var op = tf.OpDefLib._apply_op_helper("FFT2D", name: name, keywords: dict);
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return op.output;
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return tf.Context.ExecuteOp("FFT2D", name, new ExecuteOpArgs(input));
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}
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/// <summary>
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/// </remarks>
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public static Tensor f_f_t3d(Tensor input, string name = "FFT3D")
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{
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var dict = new Dictionary<string, object>();
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dict["input"] = input;
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var op = tf.OpDefLib._apply_op_helper("FFT3D", name: name, keywords: dict);
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return op.output;
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return tf.Context.ExecuteOp("FFT3D", name, new ExecuteOpArgs(input));
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}
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/// <summary>
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/// </remarks>
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public static Tensor i_f_f_t(Tensor input, string name = "IFFT")
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{
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var dict = new Dictionary<string, object>();
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dict["input"] = input;
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var op = tf.OpDefLib._apply_op_helper("IFFT", name: name, keywords: dict);
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return op.output;
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return tf.Context.ExecuteOp("IFFT", name, new ExecuteOpArgs(input));
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}
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/// <summary>
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/// </remarks>
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public static Tensor i_f_f_t2d(Tensor input, string name = "IFFT2D")
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{
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var dict = new Dictionary<string, object>();
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dict["input"] = input;
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var op = tf.OpDefLib._apply_op_helper("IFFT2D", name: name, keywords: dict);
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return op.output;
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return tf.Context.ExecuteOp("IFFT2D", name, new ExecuteOpArgs(input));
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}
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/// <summary>
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/// </remarks>
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public static Tensor i_f_f_t3d(Tensor input, string name = "IFFT3D")
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{
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var dict = new Dictionary<string, object>();
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dict["input"] = input;
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var op = tf.OpDefLib._apply_op_helper("IFFT3D", name: name, keywords: dict);
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return op.output;
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return tf.Context.ExecuteOp("IFFT3D", name, new ExecuteOpArgs(input));
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}
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/// <summary>

‎test/TensorFlowNET.Graph.UnitTest/SignalTest.cs

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using Microsoft.VisualStudio.TestTools.UnitTesting;
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using Tensorflow.NumPy;
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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 Tensorflow;
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using static Tensorflow.Binding;
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using Buffer = Tensorflow.Buffer;
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using TensorFlowNET.Keras.UnitTest;
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namespace TensorFlowNET.UnitTest.Basics
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{
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[TestClass]
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public class SignalTest : EagerModeTestBase
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{
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[TestMethod]
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public void fft()
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{
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double[] d_real = new double[] { 1.0, 2.0, 3.0, 4.0 };
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double[] d_imag = new double[] { -1.0, -3.0, 5.0, 7.0 };
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Tensor t_real = tf.constant(d_real, dtype: TF_DataType.TF_DOUBLE);
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Tensor t_imag = tf.constant(d_imag, dtype: TF_DataType.TF_DOUBLE);
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Tensor t_complex = tf.complex(t_real, t_imag);
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Tensor t_frequency_domain = tf.signal.fft(t_complex);
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Tensor f_time_domain = tf.signal.ifft(t_frequency_domain);
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Tensor t_real_result = tf.math.real(f_time_domain);
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Tensor t_imag_result = tf.math.imag(f_time_domain);
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NDArray n_real_result = t_real_result.numpy();
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NDArray n_imag_result = t_imag_result.numpy();
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double[] d_real_result = n_real_result.ToArray<double>();
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double[] d_imag_result = n_imag_result.ToArray<double>();
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Assert.IsTrue(base.Equal(d_real_result, d_real));
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Assert.IsTrue(base.Equal(d_imag_result, d_imag));
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}
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[TestMethod]
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public void fft2d()
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{
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double[] d_real = new double[] { 1.0, 2.0, 3.0, 4.0 };
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double[] d_imag = new double[] { -1.0, -3.0, 5.0, 7.0 };
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Tensor t_real = tf.constant(d_real, dtype: TF_DataType.TF_DOUBLE);
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Tensor t_imag = tf.constant(d_imag, dtype: TF_DataType.TF_DOUBLE);
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Tensor t_complex = tf.complex(t_real, t_imag);
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Tensor t_complex_2d = tf.reshape(t_complex,new int[] { 2, 2 });
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Tensor t_frequency_domain_2d = tf.signal.fft2d(t_complex_2d);
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Tensor t_time_domain_2d = tf.signal.ifft2d(t_frequency_domain_2d);
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Tensor t_time_domain = tf.reshape(t_time_domain_2d, new int[] { 4 });
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Tensor t_real_result = tf.math.real(t_time_domain);
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Tensor t_imag_result = tf.math.imag(t_time_domain);
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NDArray n_real_result = t_real_result.numpy();
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NDArray n_imag_result = t_imag_result.numpy();
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double[] d_real_result = n_real_result.ToArray<double>();
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double[] d_imag_result = n_imag_result.ToArray<double>();
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Assert.IsTrue(base.Equal(d_real_result, d_real));
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Assert.IsTrue(base.Equal(d_imag_result, d_imag));
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}
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[TestMethod]
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public void fft3d()
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{
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double[] d_real = new double[] { 1.0, 2.0, 3.0, 4.0, -3.0, -2.0, -1.0, -4.0 };
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double[] d_imag = new double[] { -1.0, -3.0, 5.0, 7.0, 6.0, 4.0, 2.0, 0.0};
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Tensor t_real = tf.constant(d_real, dtype: TF_DataType.TF_DOUBLE);
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Tensor t_imag = tf.constant(d_imag, dtype: TF_DataType.TF_DOUBLE);
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Tensor t_complex = tf.complex(t_real, t_imag);
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Tensor t_complex_3d = tf.reshape(t_complex, new int[] { 2, 2, 2 });
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Tensor t_frequency_domain_3d = tf.signal.fft2d(t_complex_3d);
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Tensor t_time_domain_3d = tf.signal.ifft2d(t_frequency_domain_3d);
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Tensor t_time_domain = tf.reshape(t_time_domain_3d, new int[] { 8 });
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Tensor t_real_result = tf.math.real(t_time_domain);
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Tensor t_imag_result = tf.math.imag(t_time_domain);
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NDArray n_real_result = t_real_result.numpy();
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NDArray n_imag_result = t_imag_result.numpy();
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double[] d_real_result = n_real_result.ToArray<double>();
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double[] d_imag_result = n_imag_result.ToArray<double>();
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Assert.IsTrue(base.Equal(d_real_result, d_real));
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Assert.IsTrue(base.Equal(d_imag_result, d_imag));
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}
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}
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}

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