AI-MXNet
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lib/AI/MXNet/NDArray.pm view on Meta::CPAN
confess("Array is not writable") if not $self->writable;
## plain number
if(not ref $value)
{
$self->_set_value($value, { out => $self });
}
# ndarray
elsif(blessed($value) and $value->isa(__PACKAGE__))
{
$value->copyto($self);
}
# slice of another ndarray
elsif(blessed($value) and $value->isa('AI::MXNet::NDArray::Slice'))
{
$value->sever->copyto($self);
}
# perl array, PDL, PDL::Matrix
else
{
$self->_sync_copyfrom($value);
}
return $self;
}
method asscalar()
{
confess("ndarray size must be 1") unless $self->size == 1;
return $self->aspdl->at(0);
}
method _sync_copyfrom(ArrayRef|PDL|PDL::Matrix $source_array)
{
my $dtype = $self->dtype;
my $pdl_type = PDL::Type->new(DTYPE_MX_TO_PDL->{ $dtype });
if(not blessed($source_array))
{
$source_array = eval {
pdl($pdl_type, $source_array);
};
confess($@) if $@;
}
if($pdl_type->numval != $source_array->type->numval)
{
my $convert_func = $pdl_type->convertfunc;
$source_array = $source_array->$convert_func;
}
$source_array = pdl($pdl_type, [@{ $source_array->unpdl } ? $source_array->unpdl->[0] : 0 ])
unless @{ $source_array->shape->unpdl };
my $pdl_shape = $source_array->shape->unpdl;
my $pdl_shape_str = join(',', ref($source_array) eq 'PDL' ? reverse @{ $pdl_shape } : @{ $pdl_shape });
my $ndary_shape_str = join(',', @{ $self->shape });
if($pdl_shape_str ne $ndary_shape_str)
{
confess("Shape inconsistant: expected $ndary_shape_str vs got $pdl_shape_str")
}
my $perl_pack_type = DTYPE_MX_TO_PERL->{$dtype};
my $buf;
## special handling for float16
if($perl_pack_type eq 'S')
{
$buf = pack("S*", map { AI::MXNetCAPI::_float_to_half($_) } unpack ("f*", ${$source_array->get_dataref}));
}
else
{
$buf = ${$source_array->get_dataref};
}
check_call(AI::MXNetCAPI::NDArraySyncCopyFromCPU($self->handle, $buf, $self->size));
return $self;
}
=head2 aspdl
Returns a copied PDL array of current array.
Returns
-------
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 => $self->writable);
}
=head2 reshape
Returns a reshaped NDArray that shares the memory with current one.
One shape dimension can be -1. In this case, the value is inferred
from the length of the array and remaining dimensions.
Parameters
lib/AI/MXNet/NDArray.pm view on Meta::CPAN
This function will return when all the pending writes to the current
NDArray are finished. There can be pending reads going on when the
function returns.
=cut
method wait_to_read()
{
check_call(AI::MXNetCAPI::NDArrayWaitToRead($self->handle));
}
=head2 shape
Get the shape of current NDArray.
Returns
-------
an array ref representing the shape of current ndarray
=cut
method shape()
{
return scalar(check_call(AI::MXNetCAPI::NDArrayGetShape($self->handle)));
}
=head2 size
Number of elements in the array.
=cut
method size(Shape|Undef $shape=)
{
my $size = 1;
map { $size *= $_ } @{ $shape//$self->shape };
return $size;
}
=head2 context
The context of the NDArray.
Returns
-------
$context : AI::MXNet::Context
=cut
method context()
{
my ($dev_type_id, $dev_id) = check_call(
AI::MXNetCAPI::NDArrayGetContext($self->handle)
);
return AI::MXNet::Context->new(
device_type => AI::MXNet::Context::devtype2str->{ $dev_type_id },
device_id => $dev_id
);
}
=head2 dtype
The data type of current NDArray.
Returns
-------
a data type string ('float32', 'float64', 'float16', 'uint8', 'int32')
representing the data type of the ndarray.
'float32' is the default dtype for the ndarray class.
=cut
method dtype()
{
my $dtype = check_call(
AI::MXNetCAPI::NDArrayGetDType(
$self->handle
)
);
return DTYPE_MX_TO_STR->{ $dtype };
}
=head2 copyto
Copy the content of current array to another entity.
When another entity is the NDArray, the content is copied over.
When another entity is AI::MXNet::Context, a new NDArray in the context
will be created.
Parameters
----------
other : NDArray or Context
Target NDArray or context we want to copy data to.
Returns
-------
dst : NDArray
=cut
method copyto(AI::MXNet::Context|AI::MXNet::NDArray $other)
{
if(blessed($other) and $other->isa('AI::MXNet::Context'))
{
my $hret = __PACKAGE__->empty(
$self->shape,
ctx => $other,
dtype => $self->dtype
);
return __PACKAGE__->_copyto($self, { out => $hret });
}
else
{
if ($other->handle eq $self->handle)
{
Carp::cluck('copy an array to itself, is it intended?');
}
return __PACKAGE__->_copyto($self, { out => $other });
}
}
=head2 copy
Makes a copy of the current ndarray in the same context
Returns
------
$copy : NDArray
=cut
method copy()
{
return $self->copyto($self->context);
}
## alias for PDL::NiceSlice
*sever = \©
=head2 T
Get transpose of the NDArray.
Works only on 2-D matrices.
=cut
method T()
{
if (@{$self->shape} > 2)
{
confess('Only 2D matrix is allowed to be transposed');
}
return __PACKAGE__->transpose($self);
}
=head2 astype
lib/AI/MXNet/NDArray.pm view on Meta::CPAN
:$dtype : Dtype, optional
The dtype of the NDArray, defaults to 'float32'.
Returns
-------
out: Array
The created NDArray.
=cut
method ones(
Shape $shape,
AI::MXNet::Context :$ctx=AI::MXNet::Context->current_ctx,
Dtype :$dtype='float32',
Maybe[AI::MXNet::NDArray] :$out=
)
{
return __PACKAGE__->_ones({ shape => $shape, ctx => "$ctx", dtype => $dtype, ($out ? (out => $out) : ()) });
}
=head2 full
Creates a new NDArray filled with given value, with specified shape.
Parameters
----------
$shape : Shape
shape of the NDArray.
val : float or int
The value to be filled with.
:$ctx : AI::MXNet::Context, optional
The context of the NDArray, defaults to current default context.
:$dtype : Dtype, optional
The dtype of the NDArray, defaults to 'float32'.
Returns
-------
out: Array
The created NDArray.
=cut
method full(
Shape $shape, Num $val,
AI::MXNet::Context :$ctx=AI::MXNet::Context->current_ctx,
Dtype :$dtype='float32', Maybe[AI::MXNet::NDArray] :$out=
)
{
return __PACKAGE__->_set_value({ src => $val, out => $out ? $out : __PACKAGE__->empty($shape, ctx => $ctx, dtype => $dtype) });
}
=head2 array
Creates a new NDArray that is a copy of the source_array.
Parameters
----------
$source_array : AI::MXNet::NDArray PDL, PDL::Matrix, Array ref in PDL::pdl format
Source data to create NDArray from.
:$ctx : AI::MXNet::Context, optional
The context of the NDArray, defaults to current default context.
:$dtype : Dtype, optional
The dtype of the NDArray, defaults to 'float32'.
Returns
-------
out: Array
The created NDArray.
=cut
method array(PDL|PDL::Matrix|ArrayRef|AI::MXNet::NDArray $source_array, AI::MXNet::Context :$ctx=AI::MXNet::Context->current_ctx, Dtype :$dtype='float32')
{
if(blessed $source_array and $source_array->isa('AI::MXNet::NDArray'))
{
my $arr = __PACKAGE__->empty($source_array->shape, ctx => $ctx, dtype => $dtype);
$arr .= $source_array;
return $arr;
}
my $pdl_type = PDL::Type->new(DTYPE_MX_TO_PDL->{ $dtype });
if(not blessed($source_array))
{
$source_array = eval {
pdl($pdl_type, $source_array);
};
confess($@) if $@;
}
$source_array = pdl($pdl_type, [@{ $source_array->unpdl } ? $source_array->unpdl->[0] : 0 ]) unless @{ $source_array->shape->unpdl };
my $shape = $source_array->shape->unpdl;
my $arr = __PACKAGE__->empty([ref($source_array) eq 'PDL' ? reverse @{ $shape } : @{ $shape }], ctx => $ctx, dtype => $dtype );
$arr .= $source_array;
return $arr;
}
=head2 concatenate
Concatenates an array ref of NDArrays along the first dimension.
Parameters
----------
$arrays : array ref of NDArrays
Arrays to be concatenate. They must have identical shape except
for the first dimension. They also must have the same data type.
:$axis=0 : int
The axis along which to concatenate.
:$always_copy=1 : bool
Default is 1. When not 1, if the arrays only contain one
NDArray, that element will be returned directly, avoid copying.
Returns
-------
An NDArray in the same context as $arrays->[0]->context.
=cut
method concatenate(ArrayRef[AI::MXNet::NDArray] $arrays, Index :$axis=0, :$always_copy=1)
{
confess("no arrays provided") unless @$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
----------
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`
$data : array ref of NDArrays or hash ref of NDArrays
The data to be saved.
=cut
method save(Str $filename, ArrayRef[AI::MXNet::NDArray]|HashRef[AI::MXNet::NDArray] $data)
{
my $handles = [];
my $names = [];
if(ref $data eq 'HASH')
{
for my $name (keys %$data)
{
push @$names, $name;
push @$handles, $data->{ $name }->handle;
}
}
else
{
@$handles = map { $_->handle } @$data;
}
check_call(
AI::MXNetCAPI::NDArraySave(
$filename,
scalar(@$handles),
$handles,
$names
)
);
}
=head2 imdecode
Decode an image from string. Requires OpenCV to work.
Parameters
----------
$str_img : str
binary image data
:$clip_rect : iterable of 4 int
clip decoded image to rectangle (x0, y0, x1, y1)
:$out= : Maybe[NDArray]
output buffer. can be 3 dimensional (c, h, w) or 4 dimensional (n, c, h, w)
:$index : int
output decoded image to i-th slice of 4 dimensional buffer
:$channels=3 : int
number of channels to output. Decode to grey scale when channels = 1.
$mean= : Maybe[NDArray]
subtract mean from decode image before outputting.
=cut
method imdecode($str_img, ArrayRef[Int] :$clip_rect=[0, 0, 0, 0],
Maybe[AI::MXNet::NDArray] :$out=, Int :$index=0, Int :$channels=3, Maybe[AI::MXNet::NDArray] :$mean=)
{
return __PACKAGE__->_imdecode(
$mean//__PACKAGE__->_new_empty_handle(),
$index,
@$clip_rect,
$channels,
length($str_img),
{ str_img => $str_img, ($out ? (out => $out) : ()) }
);
}
=head2 _new_empty_handle
Returns a new empty handle.
Empty handle can be used to hold result
Returns
-------
a new empty ndarray handle
=cut
sub _new_empty_handle
{
my $hdl = check_call(AI::MXNetCAPI::NDArrayCreateNone());
return $hdl;
}
=head2 _new_alloc_handle
Returns a new handle with specified shape and context.
Empty handle is only used to hold results
Returns
-------
a new empty ndarray handle
=cut
func _new_alloc_handle($shape, $ctx, $delay_alloc, $dtype)
{
my $hdl = check_call(AI::MXNetCAPI::NDArrayCreateEx(
$shape,
scalar(@$shape),
$ctx->device_type_id,
$ctx->device_id,
( run in 0.330 second using v1.01-cache-2.11-cpan-63c85eba8c4 )