TensorFlow.NET/src/TensorFlowNET.Core/Tensors/TensorShape.cs at master · mm86133/TensorFlow.NET · GitHub

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using NumSharp;

using System;

using System.Collections.Generic;

using System.Diagnostics.CodeAnalysis;

using System.Linq;

using System.Runtime.CompilerServices;

#if SERIALIZABLE

using Newtonsoft.Json;

#endif

using static Tensorflow.Binding;

namespace Tensorflow

{

/// <summary>

/// Represents the shape of a `Tensor`.

/// </summary>

/// <remarks>https://www.tensorflow.org/api_docs/python/tf/TensorShape</remarks>

public class TensorShape

{

private readonly Shape shape;

/// <summary>

/// Returns a list of Dimensions, or None if the shape is unspecified.

/// </summary>

public int[] dims => shape.Dimensions;

/// <summary>

/// Returns the rank of this shape.

/// </summary>

public int ndim => rank;

private int _rank;

/// <summary>

/// Returns the rank of this shape.

/// </summary>

public int rank => _rank > -1 ? shape.NDim : -1;

/// <summary>

/// Returns the size this shape represents.

/// </summary>

#if SERIALIZABLE

[JsonIgnore]

#endif

public int size

{

get

{

var dims = shape.Dimensions;

var computed = 1;

for (int i = 0; i < dims.Length; i++)

{

var val = dims[i];

if (val <= 0)

continue;

computed *= val;

}

return computed;

}

public TensorShape()

{

_rank = -1;

shape = new Shape();

}

public TensorShape(TensorShapeProto proto)

{

if (proto.UnknownRank) return;

switch (proto.Dim.Count)

{

case 0: shape = new Shape(new int[0]); break;

case 1: shape = Shape.Vector((int) proto.Dim[0].Size); break;

case 2: shape = Shape.Matrix((int) proto.Dim[0].Size, (int) proto.Dim[1].Size); break;

default:

var protodims = proto.Dim;

var len = protodims.Count;

var dims = new int[len];

for (int i = 0; i < len; i++)

dims[i] = (int) protodims[i].Size;

shape = new Shape(dims); break;

}

public TensorShape(params int[] dims)

{

switch (dims.Length)

{

case 0: shape = new Shape(new int[0]); break;

case 1: shape = Shape.Vector(dims[0]); break;

case 2: shape = Shape.Matrix(dims[0], dims[1]); break;

default: shape = new Shape(dims); break;

}

public TensorShape(int[][] dims)

{

if(dims.Length == 1)

{

switch (dims[0].Length)

{

case 0: shape = new Shape(new int[0]); break;

case 1: shape = Shape.Vector((int)dims[0][0]); break;

case 2: shape = Shape.Matrix(dims[0][0], dims[1][2]); break;

default: shape = new Shape(dims[0]); break;

}

else

{

throw new NotImplementedException("TensorShape int[][] dims");

}

/// <summary>

///

/// </summary>

/// <param name="slice"></param>

/// <returns></returns>

/// <exception cref="ArgumentException">When <see cref="Slice"/> is not an Index.</exception>

[SuppressMessage("ReSharper", "PossibleInvalidOperationException")]

public TensorShape this[Slice slice]

{

get

{

if (!slice.Stop.HasValue)

slice.Stop = dims.Length - slice.Start + 1;

if (slice.Start.HasValue == false || slice.Length.HasValue == false)

throw new ArgumentException("Slice must has Start and Length.");

return new TensorShape(dims.Skip(slice.Start.Value)

.Take(slice.Length.Value)

.ToArray());

}

/// <summary>

/// Returns True iff `self` is fully defined in every dimension.

/// </summary>

/// <returns></returns>

public bool is_fully_defined()

{

return rank > -1 && dims != null && dims.Count(x => x < 1) == 0;

}

public bool is_compatible_with(TensorShape shape2)

{

throw new NotImplementedException("TensorShape is_compatible_with");

}

[SuppressMessage("ReSharper", "ParameterHidesMember")]

public TensorShape with_rank_at_least(int rank)

{

if (rank != ndim)

throw new ValueError($"Shape {this} must have rank at least {rank}");

else

return this;

}

public TensorShape with_rank(int rank)

{

return merge_with(unknown_shape(rank: rank));

}

/// <summary>

/// Returns an unknown TensorShape, optionally with a known rank.

/// </summary>

/// <param name="rank"></param>

/// <returns></returns>

public TensorShape unknown_shape(int rank = -1)

{

if (rank == -1)

return new TensorShape(-1);

else

return new TensorShape(Enumerable.Repeat(-1, rank).ToArray());

}

/// <summary>

/// Returns the concatenation of the dimension in `self` and `other`.

/// </summary>

/// <param name="other"></param>

/// <returns></returns>

[MethodImpl(MethodImplOptions.AggressiveInlining)]

public TensorShape concatenate(int[] other)

{

return concatenate(new TensorShape(other));

}

/// <summary>

/// Returns the concatenation of the dimension in `self` and `other`.

/// </summary>

/// <param name="other"></param>

/// <returns></returns>

public TensorShape concatenate(TensorShape other)

{

var otherShape = other;

if (ndim < 0 || otherShape.ndim < 0)

return new TensorShape();

else

{

var concatenate_dims = new int[ndim + otherShape.ndim];

for (int i = 0; i < ndim; i++)

concatenate_dims[i] = dims[i];

for (int i = 0; i < otherShape.ndim; i++)

concatenate_dims[ndim + i] = otherShape.dims[i];

return new TensorShape(concatenate_dims);

}

/// <summary>

/// Returns a `TensorShape` combining the information in `self` and `other`.

/// </summary>

/// <param name="other"></param>

/// <returns></returns>

public TensorShape merge_with(TensorShape other)

{

if (dims == null)

return other;

var new_dims = new List<int>();

foreach (var i in range(ndim))

{

var dim = new Dimension(dims[i]);

var merged = dim.merge_with(new Dimension(other.dims[i]));

new_dims.Add(merged.value);

}

return new TensorShape(new_dims.ToArray());

}

/// <summary>

/// Returns a cloned array from <see cref="dims"/>.

/// </summary>

public int[] as_list() {

if (shape.IsEmpty)

throw new ValueError("as_list() is not defined on an unknown TensorShape.");

return (int[]) dims.Clone();

}

public override string ToString()

{

return shape.ToString();

}

public static implicit operator TensorShape(Shape shape) => new TensorShape((int[]) shape.Dimensions.Clone());

public static implicit operator Shape(TensorShape shape) => new Shape((int[]) shape.dims.Clone());

public static implicit operator int[](TensorShape shape) => (int[])shape.dims.Clone(); //we clone to avoid any changes

public static implicit operator TensorShape(int[] dims) => new TensorShape(dims);

public static explicit operator int(TensorShape shape) => shape.size;

public static implicit operator TensorShape(int dim) => new TensorShape(dim);

public static explicit operator (int, int)(TensorShape shape) => shape.dims.Length == 2 ? (shape.dims[0], shape.dims[1]) : (0, 0);

public static implicit operator TensorShape((int, int) dims) => new TensorShape(dims.Item1, dims.Item2);

public static explicit operator (int, int, int)(TensorShape shape) => shape.dims.Length == 3 ? (shape.dims[0], shape.dims[1], shape.dims[2]) : (0, 0, 0);

public static implicit operator TensorShape((int, int, int) dims) => new TensorShape(dims.Item1, dims.Item2, dims.Item3);

public static explicit operator (int, int, int, int)(TensorShape shape) => shape.dims.Length == 4 ? (shape.dims[0], shape.dims[1], shape.dims[2], shape.dims[3]) : (0, 0, 0, 0);

public static implicit operator TensorShape((int, int, int, int) dims) => new TensorShape(dims.Item1, dims.Item2, dims.Item3, dims.Item4);

public static explicit operator (int, int, int, int, int)(TensorShape shape) => shape.dims.Length == 5 ? (shape.dims[0], shape.dims[1], shape.dims[2], shape.dims[3], shape.dims[4]) : (0, 0, 0, 0, 0);

public static implicit operator TensorShape((int, int, int, int, int) dims) => new TensorShape(dims.Item1, dims.Item2, dims.Item3, dims.Item4, dims.Item5);

public static explicit operator (int, int, int, int, int, int)(TensorShape shape) => shape.dims.Length == 6 ? (shape.dims[0], shape.dims[1], shape.dims[2], shape.dims[3], shape.dims[4], shape.dims[5]) : (0, 0, 0, 0, 0, 0);

public static implicit operator TensorShape((int, int, int, int, int, int) dims) => new TensorShape(dims.Item1, dims.Item2, dims.Item3, dims.Item4, dims.Item5, dims.Item6);

public static explicit operator (int, int, int, int, int, int, int)(TensorShape shape) => shape.dims.Length == 7 ? (shape.dims[0], shape.dims[1], shape.dims[2], shape.dims[3], shape.dims[4], shape.dims[5], shape.dims[6]) : (0, 0, 0, 0, 0, 0, 0);

public static implicit operator TensorShape((int, int, int, int, int, int, int) dims) => new TensorShape(dims.Item1, dims.Item2, dims.Item3, dims.Item4, dims.Item5, dims.Item6, dims.Item7);

public static explicit operator (int, int, int, int, int, int, int, int)(TensorShape shape) => shape.dims.Length == 8 ? (shape.dims[0], shape.dims[1], shape.dims[2], shape.dims[3], shape.dims[4], shape.dims[5], shape.dims[6], shape.dims[7]) : (0, 0, 0, 0, 0, 0, 0, 0);

public static implicit operator TensorShape((int, int, int, int, int, int, int, int) dims) => new TensorShape(dims.Item1, dims.Item2, dims.Item3, dims.Item4, dims.Item5, dims.Item6, dims.Item7, dims.Item8);

}

TensorFlow.NET/src/TensorFlowNET.Core/Tensors/TensorShape.cs at master · mm86133/TensorFlow.NET · GitHub

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