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lib/AI/MXNet/NDArray.pm  view on Meta::CPAN

    array : PDL
        A copy of the array content.
=cut

method aspdl()
{
    my $dtype = $self->dtype;
    my $pdl_type = PDL::Type->new(DTYPE_MX_TO_PDL->{ $dtype });
    my $pdl = PDL->new_from_specification($pdl_type, reverse @{ $self->shape });
    my $perl_pack_type = DTYPE_MX_TO_PERL->{$dtype};
    my $buf = pack("$perl_pack_type*", (0)x$self->size);
    check_call(AI::MXNetCAPI::NDArraySyncCopyToCPU($self->handle, $buf, $self->size)); 
    ## special handling for float16
    if($perl_pack_type eq 'S')
    {
        $buf = pack("f*", map { AI::MXNetCAPI::_half_to_float($_) } unpack("S*", $buf));
    }
    ${$pdl->get_dataref} = $buf;
    $pdl->upd_data;
    return $pdl;
}


=head2 asmpdl

    Returns copied PDL::Matrix objectt of current array.

    Requires caller to "use PDL::Matrix" in user space.

    Returns
    -------
    array : PDL::Matrix
        A copy of array content.
=cut

method asmpdl()
{
    my $dtype = $self->dtype;
    my $pdl_type = PDL::Type->new(DTYPE_MX_TO_PDL->{ $dtype });
    my $pdl = PDL::Matrix->new_from_specification($pdl_type, @{ $self->shape });
    my $perl_pack_type = DTYPE_MX_TO_PERL->{$dtype};
    my $buf = pack("$perl_pack_type*", (0)x$self->size);
    check_call(AI::MXNetCAPI::NDArraySyncCopyToCPU($self->handle, $buf, $self->size)); 
    ## special handling for float16
    if($perl_pack_type eq 'S')
    {
        $buf = pack("f*", map { AI::MXNetCAPI::_half_to_float($_) } unpack("S*", $buf));
    }
    ${$pdl->get_dataref} = $buf;
    $pdl->upd_data;
    return $pdl;
}


=head2 _slice

    Returns sliced NDArray that shares memory with the current one.

    Parameters
    ----------
    start : int
        Starting index of slice.
    stop : int
        Finishing index of slice.
=cut

method _slice (
    Index $start,
    Index $stop
)
{
    confess("start $start > stop $stop") if $start > $stop;
    my $handle = check_call(
        AI::MXNetCAPI::NDArraySlice(
            $self->handle,
            $start,
            $stop
        )
    );
    return __PACKAGE__->new(handle => $handle, writable => $self->writable);
}

=head2  _at

    Returns a sub NDArray that shares memory with current one.

    Parameters
    ----------
    idx : int
        index of the sub array.
=cut


method _at(Index $idx)
{
    my $handle = check_call(
                AI::MXNetCAPI::NDArrayAt(
                    $self->handle, $idx >=0 ? $idx : $self->shape->[0] + $idx
                )
    );
    return __PACKAGE__->new(handle => $handle, writable => $self->writable);
}

=head2 reshape

    Returns a reshaped NDArray that shares the memory with current one.
    One shape dimension can be -1. In this case, the value is inferred
    from the length of the array and remaining dimensions.

    Parameters
    ----------
    new_shape : Shape
        new shape of NDArray
=cut

method reshape(ArrayRef[Int] $new_shape)
{
    my $i = -1;
    my @inferred = map { $i++; $_ == -1 ? ($i) : () } @$new_shape;
    assert((@inferred <= 1), 'Only one dimension can be inferred.');
    if(@inferred)
    {
        $new_shape->[$inferred[0]] = product(@{ $self->shape })/product(map { abs($_) } @{ $new_shape });
    }
    my $handle = check_call(
                    AI::MXNetCAPI::NDArrayReshape(
                        $self->handle,
                        scalar(@$new_shape),
                        $new_shape
                    )
    );
    return __PACKAGE__->new(handle => $handle, writable => $self->writable);
}

=head2 ndim

lib/AI/MXNet/NDArray.pm  view on Meta::CPAN

{
    confess("no arrays provided") unless @$arrays > 0;
    if(not $always_copy and @$arrays == 1)
    {
        return $arrays->[0];
    }
    my $shape_axis = $arrays->[0]->shape->[$axis];
    my $shape_rest1 = [@{ $arrays->[0]->shape }[0..($axis-1)]];
    my $shape_rest2 = [@{ $arrays->[0]->shape }[($axis+1)..(@{ $arrays->[0]->shape }-1)]];
    my $dtype = $arrays->[0]->dtype;
    my $i = 1;
    for my $arr (@{ $arrays }[1..(@{ $arrays }-1)])
    {
        $shape_axis += $arr->shape->[$axis];
        my $arr_shape_rest1 = [@{ $arr->shape }[0..($axis-1)]];
        my $arr_shape_rest2 = [@{ $arr->shape }[($axis+1)..(@{ $arr->shape }-1)]];
        confess("first array $arrays->[0] and $i array $arr do not match") 
            unless  join(',',@$arr_shape_rest1) eq join(',',@$shape_rest1);
        confess("first array $arrays->[0] and $i array $arr do not match") 
            unless  join(',',@$arr_shape_rest2) eq join(',',@$shape_rest2);
        confess("first array $arrays->[0] and $i array $arr dtypes do not match") 
            unless  join(',',@$arr_shape_rest2) eq join(',',@$shape_rest2);
        $i++;
    }
    my $ret_shape = [@$shape_rest1, $shape_axis, @$shape_rest2];
    my $ret = __PACKAGE__->empty($ret_shape, ctx => $arrays->[0]->context, dtype => $dtype);
    my $idx = 0;
    my $begin = [(0)x@$ret_shape];
    my $end = [@$ret_shape];
    for my $arr (@$arrays)
    {
        if ($axis == 0)
        {
            $ret->slice([$idx,($idx+$arr->shape->[0]-1)]) .= $arr;
        }
        else
        {
            $begin->[$axis] = $idx;
            $end->[$axis] = $idx+$arr->shape->[$axis];
            __PACKAGE__->_crop_assign(
                $ret, $arr, 
                { 
                    out => $ret,
                    begin => $begin,
                    end => $end
                }
            );
        }
        $idx += $arr->shape->[$axis];
    }
    return $ret
}

=head2 arange

    Similar function in the MXNet ndarray as numpy.arange
    See Also https://docs.scipy.org/doc/numpy/reference/generated/numpy.arange.html.

    Parameters
    ----------
    :$start=0 : number, optional
        Start of interval. The interval includes this value. The default start value is 0.
    $stop= : number, optional
        End of interval. The interval does not include this value.
    :$step=1 : number, optional
        Spacing between the values
    :$repeat=1 : number, optional
        The repeating time of all elements.
        E.g repeat=3, the element a will be repeated three times --> a, a, a.
    :$ctx : Context, optional
        The context of the NDArray, defaultw to current default context.
    :$dtype : data type, optional
        The value type of the NDArray, defaults to float32

    Returns
    -------
    $out : NDArray
        The created NDArray
=cut

method arange(Index :$start=0, Index :$stop=, Index :$step=1, Index :$repeat=1,
              AI::MXNet::Context :$ctx=AI::MXNet::Context->current_ctx, Dtype :$dtype='float32')
{
    return __PACKAGE__->_arange({
                start => $start,
                (defined $stop ? (stop => $stop) : ()),
                step => $step,
                repeat => $repeat,
                dtype => $dtype,
                ctx => "$ctx"
    });
}

=head2 load

    Loads ndarrays from a binary file.

    You can also use Storable to do the job if you only work with Perl.
    The advantage of load/save is the file is language agnostic.
    This means the file saved using save can be loaded by other language binding of mxnet.
    You also get the benefit being able to directly load/save from cloud storage(S3, HDFS)

    Parameters
    ----------
    fname : str
        The name of the file.Can be S3 or HDFS address (remember built with S3 support).
        Example of fname:

        - `s3://my-bucket/path/my-s3-ndarray`
        - `hdfs://my-bucket/path/my-hdfs-ndarray`
        - `/path-to/my-local-ndarray`

    Returns
    -------
    $out : array ref of NDArrays or hash ref with NDArrays
=cut

method load(Str $filename)
{
    my ($handles, $names) = check_call(AI::MXNetCAPI::NDArrayLoad($filename));
    if (not @$names)
    {
        return [map { __PACKAGE__->new(handle => $_) } @$handles];
    }
    else
    {
        my $n = @$names;
        my $h = @$handles;
        confess("Handles [$h] and names [$n] count mismatch") unless $h == $n;
        my %ret;
        @ret{ @$names } = map { __PACKAGE__->new(handle => $_) } @$handles;
        return \%ret;
    }
}

=head2 save

    Save array ref of NDArray or hash of str->NDArray to a binary file.

    You can also use Storable to do the job if you only work with Perl.
    The advantage of load/save is the file is language agnostic.
    This means the file saved using save can be loaded by other language binding of mxnet.
    You also get the benefit being able to directly load/save from cloud storage(S3, HDFS)

    Parameters