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Added Cropping and Permute (+test) · MSavameri/TensorFlow.NET@a7099db · GitHub

  1. MSavameri
  2. TensorFlow.NET

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Commit a7099db

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KevinOfCathay
authored and
Oceania2018
committed
Added Cropping and Permute (+test)
1 parent 47d0f82 commit a7099db

15 files changed

Lines changed: 604 additions & 75 deletions

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‎src/TensorFlowNET.Core/Keras/ArgsDefinition/Activation/ELUArgs.cs‎

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using System;
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using System.Collections.Generic;
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using System.Text;
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namespace Tensorflow.Keras.ArgsDefinition {
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public class ELUArgs : LayerArgs {
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public float Alpha { get; set; } = 0.1f;
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}
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}

‎src/TensorFlowNET.Core/Keras/ArgsDefinition/Cropping/Cropping2DArgs.cs‎

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using Tensorflow.NumPy;
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namespace Tensorflow.Keras.ArgsDefinition {
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public class Cropping2DArgs : LayerArgs {
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/// <summary>
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/// channel last: (b, h, w, c)
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/// channels_first: (b, c, h, w)
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/// </summary>
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public enum DataFormat { channels_first = 0, channels_last = 1 }
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/// <summary>
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/// Accept: int[1][2], int[1][1], int[2][2]
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/// </summary>
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public NDArray cropping { get; set; }
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public DataFormat data_format { get; set; } = DataFormat.channels_last;
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}
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}

‎src/TensorFlowNET.Core/Keras/ArgsDefinition/Cropping/Cropping3DArgs.cs‎

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using Tensorflow.NumPy;
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namespace Tensorflow.Keras.ArgsDefinition {
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public class Cropping3DArgs : LayerArgs {
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/// <summary>
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/// channel last: (b, h, w, c)
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/// channels_first: (b, c, h, w)
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/// </summary>
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public enum DataFormat { channels_first = 0, channels_last = 1 }
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/// <summary>
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/// Accept: int[1][3], int[1][1], int[3][2]
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/// </summary>
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public NDArray cropping { get; set; }
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public DataFormat data_format { get; set; } = DataFormat.channels_last;
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}
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}

‎src/TensorFlowNET.Core/Keras/ArgsDefinition/Cropping/CroppingArgs.cs‎

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using Tensorflow.NumPy;
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namespace Tensorflow.Keras.ArgsDefinition {
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public class CroppingArgs : LayerArgs {
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/// <summary>
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/// Accept length 1 or 2
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/// </summary>
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public NDArray cropping { get; set; }
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}
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}

‎src/TensorFlowNET.Core/Keras/ArgsDefinition/Reshaping/PermuteArgs.cs‎

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namespace Tensorflow.Keras.ArgsDefinition {
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public class PermuteArgs : LayerArgs {
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public int[] dims { get; set; }
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}
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}

‎src/TensorFlowNET.Keras/Layers/Activation/ELU.cs‎

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using System;
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using System.Collections.Generic;
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using System.Text;
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using Tensorflow.Keras.ArgsDefinition;
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using Tensorflow.Keras.Engine;
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using static Tensorflow.Binding;
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namespace Tensorflow.Keras.Layers {
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/// <summary>
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/// ELU Layer:
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/// x = 0 when x > 0, x = alpha( e^x-1 ) elsewhere
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/// </summary>
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public class ELU : Layer {
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ELUArgs args;
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float alpha => args.Alpha;
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public ELU ( ELUArgs args ) : base(args) {
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this.args = args;
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}
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protected override void build ( Tensors inputs ) {
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if ( alpha < 0f ) {
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throw new ValueError("Alpha must be a number greater than 0.");
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}
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built = true;
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}
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protected override Tensors Call ( Tensors inputs, Tensor state = null, bool? training = null ) {
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Tensor output = inputs;
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if ( alpha != 1f ) {
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output = tf.where(output > 0f, output, alpha * (tf.exp(output) - 1f));
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}
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return output;
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}
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public override Shape ComputeOutputShape ( Shape input_shape ) {
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return input_shape;
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}
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}
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}

‎src/TensorFlowNET.Keras/Layers/Activation/SELU.cs‎

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using System;
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using System.Collections.Generic;
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using System.Text;
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using Tensorflow.Keras.ArgsDefinition;
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using Tensorflow.Keras.Engine;
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using static Tensorflow.Binding;
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namespace Tensorflow.Keras.Layers {
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/// <summary>
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/// SELU Layer:
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/// similar to ELU, but has pre-defined alpha and scale
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/// </summary>
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public class SELU : Layer {
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protected const float alpha = 1.67326324f, scale = 1.05070098f;
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public SELU ( LayerArgs args ) : base(args) {
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// SELU has no arguments
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}
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protected override void build ( Tensors inputs ) {
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if ( alpha < 0f ) {
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throw new ValueError("Alpha must be a number greater than 0.");
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}
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built = true;
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}
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protected override Tensors Call ( Tensors inputs, Tensor state = null, bool? training = null ) {
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Tensor output = inputs;
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return tf.where(output > 0f, scale * output, scale * alpha * (tf.exp(output) - 1f));
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}
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public override Shape ComputeOutputShape ( Shape input_shape ) {
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return input_shape;
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}
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}
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}

‎src/TensorFlowNET.Keras/Layers/Cropping/Cropping1D.cs‎

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using Tensorflow.Keras.ArgsDefinition;
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using Tensorflow.Keras.Engine;
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namespace Tensorflow.Keras.Layers {
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public class Cropping1D : Layer {
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CroppingArgs args;
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public Cropping1D ( CroppingArgs args ) : base(args) {
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this.args = args;
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}
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protected override void build ( Tensors inputs ) {
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if ( args.cropping.rank != 1 ) {
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// throw an ValueError exception
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throw new ValueError("");
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}
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else if ( args.cropping.shape[0] > 2 || args.cropping.shape[0] < 1 ) {
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throw new ValueError("The `cropping` argument must be a tuple of 2 integers.");
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}
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built = true;
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}
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protected override Tensors Call ( Tensors inputs, Tensor state = null, bool? training = null ) {
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Tensor output = inputs;
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if ( output.rank != 3 ) {
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// throw an ValueError exception
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throw new ValueError("Expected dim=3, found dim=" + output.rank);
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}
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if ( args.cropping.shape[0] == 1 ) {
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int crop_start = args.cropping[0];
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output = output[new Slice(), new Slice(crop_start, ( int ) output.shape[1] - crop_start), new Slice()];
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}
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else {
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int crop_start = args.cropping[0], crop_end = args.cropping[1];
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output = output[new Slice(), new Slice(crop_start, ( int ) (output.shape[1]) - crop_end), new Slice()];
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}
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return output;
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}
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public override Shape ComputeOutputShape ( Shape input_shape ) {
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if ( args.cropping.shape[0] == 1 ) {
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int crop = args.cropping[0];
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return new Shape(( int ) (input_shape[0]), ( int ) (input_shape[1] - crop * 2), ( int ) (input_shape[2]));
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}
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else {
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int crop_start = args.cropping[0], crop_end = args.cropping[1];
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return new Shape(( int ) (input_shape[0]), ( int ) (input_shape[1] - crop_start - crop_end), ( int ) (input_shape[2]));
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}
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}
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}
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}

‎src/TensorFlowNET.Keras/Layers/Cropping/Cropping2D.cs‎

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using Tensorflow.Keras.ArgsDefinition;
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using Tensorflow.Keras.Engine;
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namespace Tensorflow.Keras.Layers {
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/// <summary>
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/// Crop the input along axis 1 and 2.
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/// <para> For example: </para>
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/// <para> shape (1, 5, 5, 5) -- crop2D ((1, 2), (1, 3)) --> shape (1, 2, 1, 5) </para>
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/// </summary>
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public class Cropping2D : Layer {
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Cropping2DArgs args;
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public Cropping2D ( Cropping2DArgs args ) : base(args) {
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this.args = args;
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}
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protected override void build ( Tensors inputs ) {
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built = true;
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}
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protected override Tensors Call ( Tensors inputs, Tensor state = null, bool? training = null ) {
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Tensor output = inputs;
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if ( output.rank != 4 ) {
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// throw an ValueError exception
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throw new ValueError("Expected dim=4, found dim=" + output.rank);
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}
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if ( args.cropping.shape == new Shape(1) ) {
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int crop = args.cropping[0];
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if ( args.data_format == Cropping2DArgs.DataFormat.channels_last ) {
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output = output[new Slice(),
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new Slice(crop, ( int ) output.shape[1] - crop),
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new Slice(crop, ( int ) output.shape[2] - crop),
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new Slice()];
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}
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else {
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output = output[new Slice(),
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new Slice(),
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new Slice(crop, ( int ) output.shape[2] - crop),
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new Slice(crop, ( int ) output.shape[3] - crop)];
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}
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}
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// a tuple of 2 integers
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else if ( args.cropping.shape == new Shape(2) ) {
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int crop_1 = args.cropping[0];
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int crop_2 = args.cropping[1];
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if ( args.data_format == Cropping2DArgs.DataFormat.channels_last ) {
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output = output[new Slice(),
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new Slice(crop_1, ( int ) output.shape[1] - crop_1),
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new Slice(crop_2, ( int ) output.shape[2] - crop_2),
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new Slice()];
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}
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else {
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output = output[new Slice(),
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new Slice(),
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new Slice(crop_1, ( int ) output.shape[2] - crop_1),
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new Slice(crop_2, ( int ) output.shape[3] - crop_2)];
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}
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}
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else if ( args.cropping.shape[0] == 2 && args.cropping.shape[1] == 2 ) {
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int x_start = args.cropping[0, 0], x_end = args.cropping[0, 1];
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int y_start = args.cropping[1, 0], y_end = args.cropping[1, 1];
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if ( args.data_format == Cropping2DArgs.DataFormat.channels_last ) {
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output = output[new Slice(),
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new Slice(x_start, ( int ) output.shape[1] - x_end),
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new Slice(y_start, ( int ) output.shape[2] - y_end),
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new Slice()];
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}
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else {
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output = output[new Slice(),
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new Slice(),
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new Slice(x_start, ( int ) output.shape[2] - x_end),
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new Slice(y_start, ( int ) output.shape[3] - y_end)
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];
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}
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}
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return output;
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}
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public override Shape ComputeOutputShape ( Shape input_shape ) {
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if ( args.cropping.shape == new Shape(1) ) {
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int crop = args.cropping[0];
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if ( args.data_format == Cropping2DArgs.DataFormat.channels_last ) {
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return new Shape(( int ) input_shape[0], ( int ) input_shape[1] - crop * 2, ( int ) input_shape[2] - crop * 2, ( int ) input_shape[3]);
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}
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else {
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return new Shape(( int ) input_shape[0], ( int ) input_shape[1], ( int ) input_shape[2] - crop * 2, ( int ) input_shape[3] - crop * 2);
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}
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}
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// a tuple of 2 integers
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else if ( args.cropping.shape == new Shape(2) ) {
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int crop_1 = args.cropping[0], crop_2 = args.cropping[1];
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if ( args.data_format == Cropping2DArgs.DataFormat.channels_last ) {
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return new Shape(( int ) input_shape[0], ( int ) input_shape[1] - crop_1 * 2, ( int ) input_shape[2] - crop_2 * 2, ( int ) input_shape[3]);
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}
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else {
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return new Shape(( int ) input_shape[0], ( int ) input_shape[1], ( int ) input_shape[2] - crop_1 * 2, ( int ) input_shape[3] - crop_2 * 2);
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}
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}
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else if ( args.cropping.shape == new Shape(2, 2) ) {
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int crop_1_start = args.cropping[0, 0], crop_1_end = args.cropping[0, 1];
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int crop_2_start = args.cropping[1, 0], crop_2_end = args.cropping[1, 1];
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if ( args.data_format == Cropping2DArgs.DataFormat.channels_last ) {
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return new Shape(( int ) input_shape[0], ( int ) input_shape[1] - crop_1_start - crop_1_end,
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( int ) input_shape[2] - crop_2_start - crop_2_end, ( int ) input_shape[3]);
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}
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else {
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return new Shape(( int ) input_shape[0], ( int ) input_shape[1],
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( int ) input_shape[2] - crop_1_start - crop_1_end, ( int ) input_shape[3] - crop_2_start - crop_2_end);
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}
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}
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else {
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throw new ValueError();
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}
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}
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}
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}

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