// Copyright (C) 2015 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#undef DLIB_DNn_TENSOR_ABSTRACT_H_
#ifdef DLIB_DNn_TENSOR_ABSTRACT_H_
#include "../matrix.h"
#include "../any/any_abstract.h"
namespace dlib
{
// ----------------------------------------------------------------------------------------
class tensor
{
/*!
WHAT THIS OBJECT REPRESENTS
This object represents a 4D array of float values, all stored contiguously
in memory. Importantly, it keeps two copies of the floats, one on the host
CPU side and another on the GPU device side. It automatically performs the
necessary host/device transfers to keep these two copies of the data in
sync.
All transfers to the device happen asynchronously with respect to the
default CUDA stream so that CUDA kernel computations can overlap with data
transfers. However, any transfers from the device to the host happen
synchronously in the default CUDA stream. Therefore, you should perform
all your CUDA kernel launches on the default stream so that transfers back
to the host do not happen before the relevant computations have completed.
If DLIB_USE_CUDA is not #defined then this object will not use CUDA at all.
Instead, it will simply store one host side memory block of floats.
Finally, the convention in dlib code is to interpret the tensor as a set of
num_samples() 3D arrays, each of dimension k() by nr() by nc(). Also,
while this class does not specify a memory layout, the convention is to
assume that indexing into an element at coordinates (sample,k,r,c) can be
accomplished via:
host()[((sample*t.k() + k)*t.nr() + r)*t.nc() + c]
THREAD SAFETY
Instances of this object are not thread-safe. So don't touch one from
multiple threads at the same time.
!*/
public:
virtual ~tensor();
long long num_samples(
) const;
/*!
ensures
- returns the number of 3D arrays of dimension k() by nr() by nc() there
are in this object.
!*/
long long k(
) const;
/*!
ensures
- returns the k dimension of this tensor. Generally, we think of a tensor
as containing num_samples() images of nr() by nc() rows and columns, each
with k() channels.
!*/
long long nr(
) const;
/*!
ensures
- returns the number of rows in this tensor.
!*/
long long nc(
) const;
/*!
ensures
- returns the number of columns in this tensor.
!*/
size_t size(
) const;
/*!
ensures
- returns num_samples()*k()*nr()*nc()
(i.e. the total number of floats in this tensor)
!*/
void async_copy_to_device(
) const;
/*!
ensures
- This function does not block.
- if (the host version of the data is newer than the device's copy) then
- Begins asynchronously copying host data to the device.
- A call to device() that happens before the transfer completes will
block until the transfer is complete. That is, it is safe to call
async_copy_to_device() and then immediately call device().
!*/
typedef float* iterator;
typedef const float* const_iterator;
iterator begin() { return host(); }
const_iterator begin() const { return host(); }
iterator end() { return host()+size(); }
const_iterator end() const { return host()+size(); }
/*!
ensures
- makes a tensor iterable just like the STL containers.
!*/
virtual const float* host(
) const = 0;
/*!
ensures
- returns a pointer to the host memory block of size() contiguous float
values or nullptr if size()==0.
- if (the host's copy of the data is out of date) then
- copies the data from the device to the host, while this is happening
the call to host() blocks.
!*/
virtual float* host(
) = 0;
/*!
ensures
- returns a pointer to the host memory block of size() contiguous float
values or nullptr if size()==0.
- if (the host's copy of the data is out of date) then
- copies the data from the device to the host, while this is happening
the call to host() blocks.
- Marks the device side data as out of date so that the next call to
device() will perform a host to device transfer. If you want to begin
the transfer immediately then you can call async_copy_to_device() after
calling host().
!*/
virtual float* host_write_only(
) = 0;
/*!
ensures
- This function returns the same pointer as host(), except that it never
performs a device to host memory copy. Instead, it immediately marks the
device side data as out of date, effectively discarding it. Therefore,
the values in the data pointed to by host_write_only() are undefined and
you should only call host_write_only() if you are going to assign to
every memory location in the returned memory block.
!*/
virtual const float* device(
) const = 0;
/*!
requires
- DLIB_USE_CUDA is #defined
ensures
- returns a pointer to the device memory block of size() contiguous float
values or nullptr if size()==0.
- if (the device's copy of the data is out of date) then
- copies the data from the host to the device, while this is happening
the call to device() blocks.
!*/
virtual float* device(
) = 0;
/*!
requires
- DLIB_USE_CUDA is #defined
ensures
- returns a pointer to the device memory block of size() contiguous float
values or nullptr if size()==0.
- if (the device's copy of the data is out of date) then
- copies the data from the host to the device, while this is happening
the call to device() blocks.
- Marks the host side data as out of date so that the next call to
host() will perform a device to host transfer.
!*/
virtual float* device_write_only(
) = 0;
/*!
requires
- DLIB_USE_CUDA is #defined
ensures
- This function returns the same pointer as device(), except that it never
performs a host to device memory copy. Instead, it immediately marks the
host side data as out of date, effectively discarding it. Therefore, the
values in the data pointed to by device_write_only() are undefined and
you should only call device_write_only() if you are going to assign to
every memory location in the returned memory block.
!*/
virtual const any& annotation(
) const = 0;
/*!
ensures
- returns a const reference to the any object in this tensor. The any
object can be used to store any additional annotation you like in a
tensor. However, it should be noted that the annotation() is ignored by
serialize() and therefore not saved when a tensor is serialized.
!*/
virtual any& annotation(
) = 0;
/*!
ensures
- returns a non-const reference to the any object in this tensor. The any
object can be used to store any additional annotation you like in a
tensor. However, it should be noted that the annotation() is ignored by
serialize() and therefore not saved when a tensor is serialized.
!*/
int device_id(
) const;
/*!
ensures
- returns the ID of the CUDA device that allocated this memory. I.e. the
number returned by cudaGetDevice() when the memory was allocated.
- If CUDA is not being used then this function always returns 0.
!*/
tensor& operator= (
float val
);
/*!
ensures
- sets all elements of this tensor equal to val.
- returns *this
!*/
tensor& operator*= (
float val
);
/*!
ensures
- pointwise multiplies all elements of *this tensor with val.
- returns *this
!*/
tensor& operator/= (
float val
);
/*!
ensures
- pointwise divides all elements of *this tensor with val.
- returns *this
!*/
template <typename EXP>
tensor& operator= (
const matrix_exp<EXP>& item
);
/*!
requires
- num_samples() == item.nr()
- k()*nr()*nc() == item.nc()
- item contains float values
ensures
- Assigns item to *this tensor by performing:
set_ptrm(host(), num_samples(), k()*nr()*nc()) = item;
!*/
template <typename EXP>
tensor& operator+= (
const matrix_exp<EXP>& item
);
/*!
requires
- num_samples() == item.nr()
- k()*nr()*nc() == item.nc()
- item contains float values
ensures
- Adds item to *this tensor by performing:
set_ptrm(host(), num_samples(), k()*nr()*nc()) += item;
!*/
template <typename EXP>
tensor& operator-= (
const matrix_exp<EXP>& item
);
/*!
requires
- num_samples() == item.nr()
- k()*nr()*nc() == item.nc()
- item contains float values
ensures
- Subtracts item from *this tensor by performing:
set_ptrm(host(), num_samples(), k()*nr()*nc()) -= item;
!*/
template <typename EXP>
void set_sample (
unsigned long long idx,
const matrix_exp<EXP>& item
);
/*!
requires
- idx < num_samples()
- k()*nr()*nc() == item.size()
- item contains float values
ensures
- Assigns item to the idx'th sample in *this by performing:
set_ptrm(host()+idx*item.size(), item.nr(), item.nc()) = item;
!*/
template <typename EXP>
void add_to_sample (
unsigned long long idx,
const matrix_exp<EXP>& item
);
/*!
requires
- idx < num_samples()
- k()*nr()*nc() == item.size()
- item contains float values
ensures
- Adds item to the idx'th sample in *this by performing:
set_ptrm(host()+idx*item.size(), item.nr(), item.nc()) += item;
!*/
protected:
// You can't move or copy another tensor into *this since that might modify the
// tensor's dimensions. If you want to do that sort of thing then use a
// resizable_tensor.
tensor(const tensor& item);
tensor& operator= (const tensor& item);
tensor(tensor&& item);
tensor& operator=(tensor&& item);
};
// ----------------------------------------------------------------------------------------
void memcpy (
tensor& dest,
const tensor& src
);
/*!
requires
- dest.size() == src.size()
ensures
- Copies the data in src to dest. If the device data is current on both src
and dest then the copy will happen entirely on the device side.
- It doesn't matter what GPU device is selected by cudaSetDevice(). You can
always copy tensor objects to and from each other regardless.
- This function blocks until the copy has completed.
!*/
// ----------------------------------------------------------------------------------------
bool is_vector (
const tensor& t
);
/*!
ensures
- returns true if and only if one of the following is true:
- t.size() == t.num_samples()
- t.size() == t.k()
- t.size() == t.nr()
- t.size() == t.nc()
!*/
// ----------------------------------------------------------------------------------------
const matrix_exp mat (
const tensor& t,
long long nr,
long long nc
);
/*!
requires
- nr >= 0
- nc >= 0
- nr*nc == t.size()
ensures
- returns a matrix M such that:
- M.nr() == nr
- m.nc() == nc
- for all valid r and c:
M(r,c) == t.host()[r*nc + c]
(i.e. the tensor is interpreted as a matrix laid out in memory
in row major order)
!*/
const matrix_exp mat (
const tensor& t
);
/*!
ensures
- if (t.size() != 0) then
- returns mat(t, t.num_samples(), t.size()/t.num_samples())
- else
- returns an empty matrix.
!*/
const matrix_exp image_plane (
const tensor& t,
long long sample = 0,
long long k = 0
);
/*!
requires
- t.size() != 0
- 0 <= sample < t.num_samples()
- 0 <= k < t.k()
ensures
- returns the k-th image plane from the sample-th image in t. That is,
returns a matrix M such that:
- M contains float valued elements.
- M.nr() == t.nr()
- M.nc() == t.nc()
- for all valid r and c:
- M(r,c) == t.host()[((sample*t.k() + k)*t.nr() + r)*t.nc() + c]
!*/
// ----------------------------------------------------------------------------------------
bool have_same_dimensions (
const tensor& a,
const tensor& b
);
/*!
ensures
- returns true if and only if all of the fallowing are satisfied:
- a.num_samples() == b.num_samples()
- a.k() == b.k()
- a.nr() == b.nr()
- a.nc() == b.nc()
!*/
// ----------------------------------------------------------------------------------------
class resizable_tensor : public tensor
{
/*!
WHAT THIS OBJECT REPRESENTS
This object is just a tensor with the additional ability to be resized.
!*/
public:
resizable_tensor(
);
/*!
ensures
- #size() == 0
- #num_samples() == 0
- #k() == 0
- #nr() == 0
- #nc() == 0
- #capacity() == 0
!*/
template <typename EXP>
resizable_tensor(
const matrix_exp<EXP>& item
);
/*!
requires
- item contains float values
ensures
- #num_samples() == item.nr()
- #k() == item.nc()
- #nr() == 1
- #nc() == 1
- Assigns item to *this tensor by performing:
set_ptrm(host(), num_samples(), k()*nr()*nc()) = item;
- #capacity() == size()
!*/
explicit resizable_tensor(
long long n_, long long k_ = 1, long long nr_ = 1, long long nc_ = 1
);
/*!
requires
- n_ >= 0
- k_ >= 0
- nr_ >= 0
- nc_ >= 0
ensures
- #size() == n_*k_*nr_*nc_
- #num_samples() == n_
- #k() == k_
- #nr() == nr_
- #nc() == nc_
- #capacity() == size()
!*/
// This object is copyable and movable
resizable_tensor(const resizable_tensor&) = default;
resizable_tensor(resizable_tensor&&) = default;
resizable_tensor& operator= (const resizable_tensor&) = default;
resizable_tensor& operator= (resizable_tensor&&) = default;
size_t capacity (
) const;
/*!
ensures
- returns the total number of floats allocated. This might be different
from the size() since calls to set_size() that make a tensor smaller
don't trigger reallocations. They simply adjust the nominal dimensions
while keeping the same allocated memory block. This makes calls to
set_size() very fast. If you need to deallocate a tensor then use
clear().
!*/
void clear(
);
/*!
ensures
- #size() == 0
- #num_samples() == 0
- #k() == 0
- #nr() == 0
- #nc() == 0
- #annotation().is_empty() == true
- #capacity() == 0
!*/
void copy_size (
const tensor& item
);
/*!
ensures
- resizes *this so that: have_same_dimensions(#*this, item)==true
!*/
void set_size(
long long n_, long long k_ = 1, long long nr_ = 1, long long nc_ = 1
);
/*!
requires
- n_ >= 0
- k_ >= 0
- nr_ >= 0
- nc_ >= 0
ensures
- #size() == n_*k_*nr_*nc_
- #num_samples() == n_
- #k() == k_
- #nr() == nr_
- #nc() == nc_
- #capacity() == max(#size(), capacity())
(i.e. capacity() never goes down when calling set_size().)
!*/
template <typename EXP>
resizable_tensor& operator= (
const matrix_exp<EXP>& item
);
/*!
requires
- item contains float values
ensures
- if (num_samples() == item.nr() && k()*nr()*nc() == item.nc()) then
- the dimensions of this tensor are not changed
- else
- #num_samples() == item.nr()
- #k() == item.nc()
- #nr() == 1
- #nc() == 1
- Assigns item to *this tensor by performing:
set_ptrm(host(), num_samples(), k()*nr()*nc()) = item;
!*/
};
void serialize(const tensor& item, std::ostream& out);
void deserialize(resizable_tensor& item, std::istream& in);
/*!
provides serialization support for tensor and resizable_tensor. Note that you can
serialize to/from any combination of tenor and resizable_tensor objects.
!*/
// ----------------------------------------------------------------------------------------
double dot(
const tensor& a,
const tensor& b
);
/*!
requires
- a.size() == b.size()
ensures
- returns the dot product between a and b when they are both treated as
a.size() dimensional vectors. That is, this function pointwise multiplies
the vectors together, then sums the result and returns it.
!*/
// ----------------------------------------------------------------------------------------
class alias_tensor_instance : public tensor
{
/*!
WHAT THIS OBJECT REPRESENTS
This object is a tensor that aliases another tensor. That is, it doesn't
have its own block of memory but instead simply holds pointers to the
memory of another tensor object. It therefore allows you to efficiently
break a tensor into pieces and pass those pieces into functions.
An alias_tensor_instance doesn't own the resources it points to in any sense.
So it is important to make sure that the underlying owning tensor doesn't get
destructed before any alias tensors which point to it are destructed.
!*/
// You can't default initialize this object. You can only get instances of it from
// alias_tensor::operator().
alias_tensor_instance(
);
};
class alias_tensor_const_instance
{
/*!
WHAT THIS OBJECT REPRESENTS
This is essentially a const version of alias_tensor_instance and therefore
represents a tensor. However, due to the mechanics of C++, this object
can't inherit from tensor. So instead it provides a get() and an implicit
conversion to const tensor.
!*/
public:
// non-const alias tensors are convertible to const ones.
alias_tensor_const_instance(const alias_tensor_instance& item);
// Methods that cast the alias to a tensor.
const tensor& get() const;
operator const tensor& ();
private:
// You can't default initialize this object. You can only get instances of it from
// alias_tensor::operator().
alias_tensor_const_instance();
};
class alias_tensor
{
/*!
WHAT THIS OBJECT REPRESENTS
This is a tool for creating tensor objects that alias other tensor objects.
That is, it allows you to make a tensor that references the memory space of
another tensor object rather than owning its own memory. This allows you
to do things like interpret a single tensor in different ways or even as a
group of multiple tensors.
!*/
public:
alias_tensor (
);
/*!
ensures
- #size() == 0
- #num_samples() == 0
- #k() == 0
- #nr() == 0
- #nc() == 0
!*/
alias_tensor (
long long n_, long long k_ = 1, long long nr_ = 1, long long nc_ = 1
);
/*!
requires
- n_ >= 0
- k_ >= 0
- nr_ >= 0
- nc_ >= 0
ensures
- #size() == n_*k_*nr_*nc_
- #num_samples() == n_
- #k() == k_
- #nr() == nr_
- #nc() == nc_
!*/
long long num_samples() const;
long long k() const;
long long nr() const;
long long nc() const;
size_t size() const;
alias_tensor_instance operator() (
tensor& t,
size_t offset = 0
) const;
/*!
requires
- offset+size() <= t.size()
ensures
- Returns a tensor that simply aliases the elements of t beginning with t's
offset'th element. Specifically, this function returns an aliasing
tensor T such that:
- T.size() == size()
- T.num_samples() == num_samples()
- T.k() == k()
- T.nr() == nr()
- T.nc() == nc()
- T.host() == t.host()+offset
- T.device() == t.device()+offset
- &T.annotation() == &t.annotation()
!*/
alias_tensor_const_instance operator() (
const tensor& t,
size_t offset = 0
) const;
/*!
requires
- offset+size() <= t.size()
ensures
- This function is identical to the above version of operator() except that
it takes and returns const tensors instead of non-const tensors.
!*/
};
void serialize(const alias_tensor& item, std::ostream& out);
void deserialize(alias_tensor& item, std::istream& in);
/*!
provides serialization support for alias_tensor.
!*/
// ----------------------------------------------------------------------------------------
}
#endif // DLIB_DNn_TENSOR_ABSTRACT_H_