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Moved the discussion of matrix expressions into its own example file. Also 

expanded it with examples of how to create new matrix expressions.

--HG--
extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%403753
This commit is contained in:
Davis King 2010-07-18 19:57:34 +00:00
parent 5a019f0a04
commit 5724b4b45a
3 changed files with 399 additions and 111 deletions
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1
examples/CMakeLists.txt
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@ -47,6 +47,7 @@ add_example(linear_manifold_regularizer_ex)
add_example(logger_ex)
add_example(logger_ex_2)
add_example(matrix_ex)
add_example(matrix_expressions_ex)
add_example(member_function_pointer_ex)
add_example(mlp_ex)
add_example(model_selection_ex)

111
examples/matrix_ex.cpp
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@ -3,10 +3,6 @@
/*
This is an example illustrating the use of the matrix object
from the dlib C++ Library.
This file also contains a discussion of the template expression
technique and how it is used by this library.
*/
@ -248,113 +244,6 @@ int main()
// MATLAB: var = min(min(A))
var = min(A);
// ------------------------- Template Expressions -----------------------------
// Now I will discuss the "template expressions" technique and how it is
// used in the matrix object. First consider the following expression:
x = y + y;
/*
Normally this expression results in machine code that looks, at a high
level, like the following:
temp = y + y;
x = temp
Temp is a temporary matrix returned by the overloaded + operator.
temp then contains the result of adding y to itself. The assignment
operator copies the value of temp into x and temp is then destroyed while
the blissful C++ user never sees any of this.
This is, however, totally inefficient. In the process described above
you have to pay for the cost of constructing a temporary matrix object
and allocating its memory. Then you pay the additional cost of copying
it over to x. It also gets worse when you have more complex expressions
such as x = round(y + y + y + M*y) which would involve the creation and copying
of 5 temporary matrices.
All these inefficiencies are removed by using the template expressions
technique. The exact details of how the technique is performed are well
outside the scope of this example but the basic idea is as follows. Instead
of having operators and functions return temporary matrix objects you
return a special object that represents the expression you wish to perform.
So consider the expression x = y + y again. With dlib::matrix what happens
is the expression y+y returns a matrix_exp object instead of a temporary matrix.
The construction of a matrix_exp does not allocate any memory or perform any
computations. The matrix_exp however has an interface that looks just like a
dlib::matrix object and when you ask it for the value of one of its elements
it computes that value on the spot. Only in the assignment operator does
someone ask the matrix_exp for these values so this avoids the use of any
temporary matrices. Thus the statement x = y + y is equivalent to the following
code:
// loop over all elements in y matrix
for (long r = 0; r < y.nr(); ++r)
for (long c = 0; c < y.nc(); ++c)
x(r,c) = y(r,c) + y(r,c);
This technique works for expressions of arbitrary complexity. So if you
typed x = round(y + y + y + M*y) it would involve no temporary matrices being
created at all. Each operator takes and returns only matrix_exp objects.
Thus, no computations are performed until the assignment operator requests
the values from the matrix_exp it receives as input.
There is, however, a slight complication in all of this. It is for statements
that involve the multiplication of a complex matrix_exp such as the following:
*/
x = M*(M+M+M+M+M+M+M);
/*
According to the discussion above, this statement would compute the value of
M*(M+M+M+M+M+M+M) totally without any temporary matrix objects. This sounds
good but we should take a closer look. Consider that the + operator is
invoked 6 times. This means we have something like this:
x = M * (matrix_exp representing M+M+M+M+M+M+M);
M is being multiplied with a quite complex matrix_exp. Now recall that when
you ask a matrix_exp what the value of any of its elements are it computes
their values *right then*.
If you think on what is involved in performing a matrix multiply you will
realize that each element of a matrix is accessed M.nr() times. In the
case of our above expression the cost of accessing an element of the
matrix_exp on the right hand side is the cost of doing 6 addition operations.
Thus, it would be faster to assign M+M+M+M+M+M+M to a temporary matrix and then
multiply that by M. This is exactly what the dlib::matrix does under the covers.
This is because it is able to spot expressions where the introduction of a
temporary is needed to speed up the computation and it will automatically do this
for you.
Another complication that is dealt with automatically is aliasing. Consider
the following expressions:
(1) M = M + M
(2) B = M * M.
(3) M = M * M.
Expressions (1) and (3) are an example of aliasing and expression (3) is also
an example of destructive aliasing.
Expression (1) can and does operate without introducing any temporaries even though
there is aliasing present in the expression. The result is loaded straight into M
using the template expression techniques described above. Expression (2) also
operates without any temporaries being introduced since there isn't any aliasing at all.
Expression (3) however contains destructive aliasing. This is because we can't
change any of the values in the M matrix without corrupting the ultimate result of
the matrix multiply. So we need to introduce a temporary. These situations are
dealt with by dlib::matrix automatically. Moreover, it can tell the different between
simple aliasing and destructive aliasing and will only introduce temporaries when
they are necessary.
*/
}
// ----------------------------------------------------------------------------------------

398
examples/matrix_expressions_ex.cpp Normal file
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@ -0,0 +1,398 @@
// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
/*
This example contains a detailed discussion of the template expression
technique used to implement the matrix tools in the dlib C++ library.
It also gives examples showing how a user can create their own custom
matrix expressions.
Note that you should be familiar with the dlib::matrix before reading
this example. So if you haven't done so already you should read the
matrix_ex.cpp example program.
*/
#include <iostream>
#include "dlib/matrix.h"
using namespace dlib;
using namespace std;
// ----------------------------------------------------------------------------------------
void custom_matrix_expressions_example();
// ----------------------------------------------------------------------------------------
int main()
{
// Declare some variables used below
matrix<double,3,1> y;
matrix<double,3,3> M;
matrix<double> x;
// set all elements to 1
y = 1;
M = 1;
x = 1;
// ------------------------- Template Expressions -----------------------------
// Now I will discuss the "template expressions" technique and how it is
// used in the matrix object. First consider the following expression:
x = y + y;
/*
Normally this expression results in machine code that looks, at a high
level, like the following:
temp = y + y;
x = temp
Temp is a temporary matrix returned by the overloaded + operator.
temp then contains the result of adding y to itself. The assignment
operator copies the value of temp into x and temp is then destroyed while
the blissful C++ user never sees any of this.
This is, however, totally inefficient. In the process described above
you have to pay for the cost of constructing a temporary matrix object
and allocating its memory. Then you pay the additional cost of copying
it over to x. It also gets worse when you have more complex expressions
such as x = round(y + y + y + M*y) which would involve the creation and copying
of 5 temporary matrices.
All these inefficiencies are removed by using the template expressions
technique. The basic idea is as follows, instead of having operators and
functions return temporary matrix objects you return a special object that
represents the expression you wish to perform.
So consider the expression x = y + y again. With dlib::matrix what happens
is the expression y+y returns a matrix_exp object instead of a temporary matrix.
The construction of a matrix_exp does not allocate any memory or perform any
computations. The matrix_exp however has an interface that looks just like a
dlib::matrix object and when you ask it for the value of one of its elements
it computes that value on the spot. Only in the assignment operator does
someone ask the matrix_exp for these values so this avoids the use of any
temporary matrices. Thus the statement x = y + y is equivalent to the following
code:
// loop over all elements in y matrix
for (long r = 0; r < y.nr(); ++r)
for (long c = 0; c < y.nc(); ++c)
x(r,c) = y(r,c) + y(r,c);
This technique works for expressions of arbitrary complexity. So if you
typed x = round(y + y + y + M*y) it would involve no temporary matrices being
created at all. Each operator takes and returns only matrix_exp objects.
Thus, no computations are performed until the assignment operator requests
the values from the matrix_exp it receives as input.
There is, however, a slight complication in all of this. It is for statements
that involve the multiplication of a complex matrix_exp such as the following:
*/
x = M*(M+M+M+M+M+M+M);
/*
According to the discussion above, this statement would compute the value of
M*(M+M+M+M+M+M+M) totally without any temporary matrix objects. This sounds
good but we should take a closer look. Consider that the + operator is
invoked 6 times. This means we have something like this:
x = M * (matrix_exp representing M+M+M+M+M+M+M);
M is being multiplied with a quite complex matrix_exp. Now recall that when
you ask a matrix_exp what the value of any of its elements are it computes
their values *right then*.
If you think on what is involved in performing a matrix multiply you will
realize that each element of a matrix is accessed M.nr() times. In the
case of our above expression the cost of accessing an element of the
matrix_exp on the right hand side is the cost of doing 6 addition operations.
Thus, it would be faster to assign M+M+M+M+M+M+M to a temporary matrix and then
multiply that by M. This is exactly what the dlib::matrix does under the covers.
This is because it is able to spot expressions where the introduction of a
temporary is needed to speed up the computation and it will automatically do this
for you.
Another complication that is dealt with automatically is aliasing. All matrix
expressions are said to "alias" their contents. For example, consider
the following expressions:
M + M
M * M
We say that the expressions (M + M) and (M * M) alias M. Additionally, we say that
the expression (M * M) destructively aliases M.
To understand why we say (M * M) destructively aliases M consider what would happen
if we attempted to evaluate it without first assigning (M * M) to a temporary matrix.
That is, if we attempted to perform the following:
for (long r = 0; r < M.nr(); ++r)
for (long c = 0; c < M.nc(); ++c)
M(r,c) = rowm(M,r)*colm(M,c);
It is clear that the result would be corrupted and M wouldn't end up with the right
values in it. So in this case we must perform the following:
temp = M*M;
M = temp;
This sort of interaction is what defines destructive aliasing. Whenever we are
assigning a matrix expression to a destination that is destructively aliased by
the expression we need to introduce a temporary. The dlib::matrix is capable of
recognizing the two forms of aliasing and introduces temporary matrices only when
necessary.
*/
// Next we discuss how to create custom matrix expressions. In what follows we
// will define three different matrix expressions and show their use.
custom_matrix_expressions_example();
}
// ----------------------------------------------------------------------------------------
// ----------------------------------------------------------------------------------------
// ----------------------------------------------------------------------------------------
template <typename M>
struct example_op_trans
{
/*!
This object defines a matrix expression that represents a transposed matrix.
As discussed above, constructing this object doesn't compute anything. It just
holds a reference to a matrix and presents an interface which defines
matrix transposition.
!*/
// Here we simply hold a reference to the matrix we are supposed to be the transpose of.
example_op_trans( const M& m_) : m(m_){}
const M& m;
// The cost field is used by matrix multiplication code to decide if a temporary needs to
// be introduced. It represents the computational cost of evaluating an element of the
// matrix expression. In this case we say that the cost of obtaining an element of the
// transposed matrix is the same as obtaining an element of the original matrix (since
// transpose doesn't really compute anything).
const static long cost = M::cost;
// Here we define the matrix expression's compile-time known dimensions. Since this
// is a transpose we define them to be the reverse of M's dimensions.
const static long NR = M::NC;
const static long NC = M::NR;
// Define the type of element in this matrix expression. Also define the
// memory manager type used and the layout. Usually we use the same types as the
// input matrix.
typedef typename M::type type;
typedef typename M::mem_manager_type mem_manager_type;
typedef typename M::layout_type layout_type;
// This is where the action is. This function is what defines the value of an element of
// this matrix expression. Here we are saying that m(c,r) == trans(m)(r,c) which is just
// the definition of transposition. Note also that we must define the return type from this
// function as a typedef. This typedef lets us either return our argument by value or by
// reference. In this case we use the same type as the underlying m matrix. Later in this
// example program you will see two other options.
typedef typename M::const_ret_type const_ret_type;
const_ret_type apply (long r, long c) const { return m(c,r); }
// Define the run-time defined dimensions of this matrix.
long nr () const { return m.nc(); }
long nc () const { return m.nr(); }
// Recall the discussion of aliasing. Each matrix expression needs to define what
// kind of aliasing it introduces so that we know when to introduce temporaries. The
// aliases() function indicates that the matrix transpose expression aliases item if
// and only if the m matrix aliases item.
template <typename U> bool aliases ( const matrix_exp<U>& item) const { return m.aliases(item); }
// This next function indicates that the matrix transpose expression also destructively
// aliases anything m aliases. I.e. transpose has destructive aliasing.
template <typename U> bool destructively_aliases ( const matrix_exp<U>& item) const { return m.aliases(item); }
};
// Here we define a simple function that creates and returns transpose expressions. Note that the
// matrix_op<> template is a matrix_exp object and exists solely to reduce the amount of boilerplate
// you have to write to create a matrix expression.
template < typename M >
const matrix_op<example_op_trans<M> > example_trans (
const matrix_exp<M>& m
)
{
typedef example_op_trans<M> op;
// m.ref() returns a reference to the object of type M contained in the matrix expression m.
return matrix_op<op>(op(m.ref()));
}
// ----------------------------------------------------------------------------------------
template <typename T>
struct example_op_vector_to_matrix
{
/*!
This object defines a matrix expression that holds a reference to a std::vector<T>
and makes it look like a column vector. Thus it enables you to use a std::vector
as if it was a dlib::matrix.
!*/
example_op_vector_to_matrix( const std::vector<T>& vect_) : vect(vect_){}
const std::vector<T>& vect;
// This expression wraps direct memory accesses so we use the lowest possible cost.
const static long cost = 1;
const static long NR = 0; // We don't know the length of the vector until runtime. So we put 0 here.
const static long NC = 1; // We do know that it only has one column (since it's a vector)
typedef T type;
// Since the std::vector doesn't use a dlib memory manager we list the default one here.
typedef memory_manager<char>::kernel_1a mem_manager_type;
// The layout type also doesn't really matter in this case. So we list row_major_layout
// since it is a good default.
typedef row_major_layout layout_type;
// Note that we define const_ret_type to be a reference type. This way we can
// return the contents of the std::vector by reference.
typedef const T& const_ret_type;
const_ret_type apply (long r, long ) const { return vect[r]; }
long nr () const { return vect.size(); }
long nc () const { return 1; }
// This expression never aliases anything since it doesn't contain any matrix expression (it
// contains only a std::vector which doesn't count since you can't assign a matrix expression
// to a std::vector object).
template <typename U> bool aliases ( const matrix_exp<U>& ) const { return false; }
template <typename U> bool destructively_aliases ( const matrix_exp<U>& ) const { return false; }
};
template < typename T >
const matrix_op<example_op_vector_to_matrix<T> > example_vector_to_matrix (
const std::vector<T>& vector
)
{
typedef example_op_vector_to_matrix<T> op;
return matrix_op<op>(op(vector));
}
// ----------------------------------------------------------------------------------------
template <typename M, typename T>
struct example_op_add_scalar
{
/*!
This object defines a matrix expression that represents a matrix with a single
scalar value added to all its elements.
!*/
example_op_add_scalar( const M& m_, const T& val_) : m(m_), val(val_){}
// A reference to the matrix
const M& m;
// A copy of the scalar value that should be added to each element of m
const T val;
// This time we add 1 to the cost since evaluating an element of this
// expression means performing 1 addition operation.
const static long cost = M::cost + 1;
const static long NR = M::NR;
const static long NC = M::NC;
typedef typename M::type type;
typedef typename M::mem_manager_type mem_manager_type;
typedef typename M::layout_type layout_type;
// Note that we declare const_ret_type to be a non-reference type. This is important
// since apply() computes a new temporary value and thus we can't return a reference
// to it.
typedef type const_ret_type;
const_ret_type apply (long r, long c) const { return m(r,c) + val; }
long nr () const { return m.nr(); }
long nc () const { return m.nc(); }
// This expression aliases anything m aliases.
template <typename U> bool aliases ( const matrix_exp<U>& item) const { return m.aliases(item); }
// Unlike the transpose expression. This expression only destructively aliases something if m does.
// So this expression has the regular non-destructive kind of aliasing.
template <typename U> bool destructively_aliases ( const matrix_exp<U>& item) const { return m.destructively_aliases(item); }
};
template < typename M, typename T >
const matrix_op<example_op_add_scalar<M,T> > add_scalar (
const matrix_exp<M>& m,
T val
)
{
typedef example_op_add_scalar<M,T> op;
return matrix_op<op>(op(m.ref(), val));
}
// ----------------------------------------------------------------------------------------
void custom_matrix_expressions_example(
)
{
matrix<double> x(2,3);
x = 1, 1, 1,
2, 2, 2;
cout << x << endl;
// Finally, lets use the matrix expressions we defined above.
// prints the transpose of x
cout << example_trans(x) << endl;
// prints this:
// 11 11 11
// 12 12 12
cout << add_scalar(x, 10) << endl;
// matrix expressions can be nested, even the user defined ones.
// the following statement prints this:
// 11 12
// 11 12
// 11 12
cout << example_trans(add_scalar(x, 10)) << endl;
// Since we setup the alias detection correctly we can even do this:
x = example_trans(add_scalar(x, 10));
cout << "new x:\n" << x << endl;
cout << "Do some testing with the example_vector_to_matrix() function: " << endl;
std::vector<float> vect;
vect.push_back(1);
vect.push_back(3);
vect.push_back(5);
// Now lets treat our std::vector like a matrix and print some things.
cout << example_vector_to_matrix(vect) << endl;
cout << add_scalar(example_vector_to_matrix(vect), 10) << endl;
/*
As an aside, note that dlib contains functions equivalent to example_trans() and
example_vector_to_matrix() already. They are:
- dlib::trans()
- dlib::vector_to_matrix()
Also, if you are going to be creating your own matrix expression you should also
look through the matrix code in the dlib/matrix folder. There you will find
many other examples of matrix expressions.
*/
}
// ----------------------------------------------------------------------------------------