AI-TensorFlow-Libtensorflow
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lib/AI/TensorFlow/Libtensorflow/Lib/FFIType/TFPtrPtrLenSizeArrayRefScalar.pm view on Meta::CPAN
# ABSTRACT: Type to hold string list as void** strings, size_t* lengths, int num_items
$AI::TensorFlow::Libtensorflow::Lib::FFIType::TFPtrPtrLenSizeArrayRefScalar::VERSION = '0.0.7';
use strict;
use warnings;
# TODO implement this
sub perl_to_native {
...
}
sub perl_to_native_post {
...
}
sub ffi_custom_type_api_1 {
{
'native_type' => 'opaque',
'perl_to_native' => \&perl_to_native,
'perl_to_native_post' => \&perl_to_native_post,
argument_count => 3,
}
}
1;
__END__
=pod
lib/AI/TensorFlow/Libtensorflow/Lib/FFIType/TFPtrSizeScalar.pm view on Meta::CPAN
? \&arguments_set_uint32
: \&arguments_set_uint64;
sub perl_to_native {
my($pointer, $size) = scalar_to_buffer($_[0]);
push @stack, [ $pointer, $size ];
arguments_set_pointer $_[1], $pointer;
arguments_set_size_t($_[1]+1, $size);
}
sub perl_to_native_post {
my($pointer, $size) = @{ pop @stack };
();
}
sub ffi_custom_type_api_1
{
{
native_type => 'opaque',
perl_to_native => \&perl_to_native,
perl_to_native_post => \&perl_to_native_post,
argument_count => 2,
}
}
1;
__END__
=pod
lib/AI/TensorFlow/Libtensorflow/Lib/FFIType/TFPtrSizeScalarRef.pm view on Meta::CPAN
my ($pointer, $size) = defined $$value
? scalar_to_buffer($$value)
: (0, 0);
push @stack, [ $value, $pointer, $size ];
arguments_set_pointer( $i , $pointer);
arguments_set_size_t( $i+1, $size);
}
sub perl_to_native_post {
pop @stack;
();
}
sub ffi_custom_type_api_1 {
{
'native_type' => 'opaque',
'perl_to_native' => \&perl_to_native,
'perl_to_native_post' => \&perl_to_native_post,
argument_count => 2,
}
}
1;
__END__
=pod
lib/AI/TensorFlow/Libtensorflow/Lib/FFIType/Variant/PackableArrayRef.pm view on Meta::CPAN
unless defined $value && ref $value eq 'ARRAY';
my $data = pack $arguments{pack_type} . '*', @$value;
my $n = scalar @$value;
my ($pointer, $size) = scalar_to_buffer($data);
push @stack, [ \$data, $pointer, $size ];
arguments_set_pointer( $i , $pointer);
arguments_set_sint32( $i+1, $n);
};
my $perl_to_native_post = install perl_to_native_post => sub {
my ($data_ref, $pointer, $size) = @{ pop @stack };
$$data_ref = buffer_to_scalar($pointer, $size);
@{$_[0]} = unpack $arguments{pack_type} . '*', $$data_ref;
();
};
install ffi_custom_type_api_1 => sub {
{
native_type => 'opaque',
argument_count => 2,
perl_to_native => $perl_to_native,
perl_to_native_post => $perl_to_native_post,
}
};
}
sub make_variant_package_name {
my ($class, $package, %arguments) = @_;
$package = "AI::TensorFlow::Libtensorflow::Lib::FFIType::TF${package}";
die "Won't clobber $package" if $INC{module_notional_filename $package};
return $package;
}
lib/AI/TensorFlow/Libtensorflow/Lib/FFIType/Variant/PackableMaybeArrayRef.pm view on Meta::CPAN
} else {
my $data = undef;
my $n = -1;
my ($pointer, $size) = (0, 0);
push @stack, [ \$data, $pointer, $size ];
arguments_set_pointer( $i , $pointer);
arguments_set_sint32( $i+1, $n);
}
};
my $perl_to_native_post = install perl_to_native_post => sub {
my ($data_ref, $pointer, $size) = @{ pop @stack };
if( ! Scalar::Util::readonly($_[0]) ) {
$$data_ref = buffer_to_scalar($pointer, $size);
@{$_[0]} = unpack $arguments{pack_type} . '*', $$data_ref;
}
();
};
install ffi_custom_type_api_1 => sub {
{
native_type => 'opaque',
argument_count => 2,
perl_to_native => $perl_to_native,
perl_to_native_post => $perl_to_native_post,
}
};
}
sub make_variant_package_name {
my ($class, $package, %arguments) = @_;
$package = "AI::TensorFlow::Libtensorflow::Lib::FFIType::TF${package}";
die "Won't clobber $package" if $INC{module_notional_filename $package};
return $package;
}
lib/AI/TensorFlow/Libtensorflow/Lib/FFIType/Variant/RecordArrayRef.pm view on Meta::CPAN
my ($value, $i) = @_;
my $data = pack "(a*)*", map $$_, @$value;
my($pointer, $size) = scalar_to_buffer($data);
my $n = @$value;
my $sizeof = $size / $n;
push @stack, [ \$data, $n, $pointer, $size , $sizeof ];
arguments_set_pointer $i , $pointer;
arguments_set_sint32 $i+1, $n if $with_size;
};
my $perl_to_native_post = install perl_to_native_post => sub {
my($data_ref, $n, $pointer, $size, $sizeof) = @{ pop @stack };
$$data_ref = buffer_to_scalar($pointer, $size);
@{$_[0]} = map {
bless \$_, $record_module
} unpack "(a${sizeof})*", $$data_ref;
();
};
install ffi_custom_type_api_1 => sub {
{
native_type => 'opaque',
argument_count => ($with_size ? 2 : 1),
perl_to_native => $perl_to_native,
perl_to_native_post => $perl_to_native_post,
}
};
}
sub make_variant_package_name {
my ($class, $package, %arguments) = @_;
$package = "AI::TensorFlow::Libtensorflow::Lib::FFIType::TF${package}";
die "Won't clobber $package" if $INC{module_notional_filename $package};
return $package;
}
lib/AI/TensorFlow/Libtensorflow/Manual/Notebook/InferenceUsingTFHubCenterNetObjDetect.pod view on Meta::CPAN
## DO NOT EDIT. Generated from notebook/InferenceUsingTFHubCenterNetObjDetect.ipynb using ./maint/process-notebook.pl.
use strict;
use warnings;
use utf8;
use constant IN_IPERL => !! $ENV{PERL_IPERL_RUNNING};
no if IN_IPERL, warnings => 'redefine'; # fewer messages when re-running cells
use feature qw(say state postderef);
use Syntax::Construct qw(each-array);
use lib::projectroot qw(lib);
BEGIN {
if( IN_IPERL ) {
$ENV{TF_CPP_MIN_LOG_LEVEL} = 3;
}
require AI::TensorFlow::Libtensorflow;
}
lib/AI/TensorFlow/Libtensorflow/Manual/Notebook/InferenceUsingTFHubCenterNetObjDetect.pod view on Meta::CPAN
=head2 Load the library
First, we need to load the C<AI::TensorFlow::Libtensorflow> library and more helpers. We then create an C<AI::TensorFlow::Libtensorflow::Status> object and helper function to make sure that the calls to the C<libtensorflow> C library are working prop...
use strict;
use warnings;
use utf8;
use constant IN_IPERL => !! $ENV{PERL_IPERL_RUNNING};
no if IN_IPERL, warnings => 'redefine'; # fewer messages when re-running cells
use feature qw(say state postderef);
use Syntax::Construct qw(each-array);
use lib::projectroot qw(lib);
BEGIN {
if( IN_IPERL ) {
$ENV{TF_CPP_MIN_LOG_LEVEL} = 3;
}
require AI::TensorFlow::Libtensorflow;
}
lib/AI/TensorFlow/Libtensorflow/Manual/Notebook/InferenceUsingTFHubEnformerGeneExprPredModel.pod view on Meta::CPAN
=pod
=encoding UTF-8
=head1 NAME
AI::TensorFlow::Libtensorflow::Manual::Notebook::InferenceUsingTFHubEnformerGeneExprPredModel - Using TensorFlow to do gene expression prediction using a pre-trained model
=head1 SYNOPSIS
The following tutorial is based on the L<Enformer usage notebook|https://github.com/deepmind/deepmind-research/blob/master/enformer/enformer-usage.ipynb>. It uses a pre-trained model based on a transformer architecture trained as described in Avsec e...
Running the code requires an Internet connection to download the model (from Google servers) and datasets (from GitHub, UCSC, and NIH).
Some of this code is identical to that of C<InferenceUsingTFHubMobileNetV2Model> notebook. Please look there for explanation for that code. As stated there, this will later be wrapped up into a high-level library to hide the details behind an API.
B<NOTE>: If running this model, please be aware that
=over
=item *
maint/inc/Pod/Elemental/Transformer/TF_Sig.pm view on Meta::CPAN
package Pod::Elemental::Transformer::TF_Sig;
# ABSTRACT: TensorFlow signatures
use Moose;
extends 'Pod::Elemental::Transformer::List';
use feature qw{ postderef };
use lib 'lib';
use AI::TensorFlow::Libtensorflow::Lib;
use AI::TensorFlow::Libtensorflow::Lib::Types qw(-all);
use Types::Standard qw(Maybe Str Int ArrayRef CodeRef ScalarRef Ref);
use Types::Encodings qw(Bytes);
use Type::Registry qw(t);
use namespace::autoclean;
sub __is_xformable {
maint/process-capi.pl view on Meta::CPAN
use FindBin;
use lib "$FindBin::Bin/../lib";
use Sub::Uplevel; # place early to override caller()
package TF::CAPI::Extract {
use Mu;
use CLI::Osprey;
use AI::TensorFlow::Libtensorflow::Lib;
use feature qw(say postderef);
use Syntax::Construct qw(heredoc-indent);
use Function::Parameters;
use Path::Tiny;
use Types::Path::Tiny qw/Path/;
use File::Find::Rule;
use Sort::Key::Multi qw(iikeysort);
use List::Util qw(uniq first);
use List::SomeUtils qw(firstidx part);
maint/process-capi.pl view on Meta::CPAN
use Mu::Role;
use Sub::Uplevel qw(uplevel);
use Hook::LexWrap;
ro _attached_functions => ( default => sub { {} } );
around attach => sub {
my ($orig, $self, $name) = @_;
my $real_name;
wrap 'FFI::Platypus::DL::dlsym',
post => sub { $real_name = $_[1] if $_[-1] };
my $ret = uplevel 3, $orig, @_[1..$#_];
push $self->_attached_functions->{$real_name}->@*, {
c => $real_name,
package => (caller(2))[0],
perl => ref($name) ? $name->[1] : $name,
args => $_[3],
return => $_[4],
};
$ret;
}
maint/process-notebook.pl view on Meta::CPAN
#if grep -C5 -P '\s+\\n' $SRC -m 2; then
#echo -e "Notebook $SRC has whitespace"
#exit 1
#fi
## Run the notebook
#jupyter nbconvert --execute --inplace $SRC
## Clean up metadata (changed by the previous nbconvert --execute)
## See more at <https://timstaley.co.uk/posts/making-git-and-jupyter-notebooks-play-nice/>
jq --indent 1 '
del(.cells[].metadata | .execution)
' $SRC | sponge $SRC
### Notice about generated file
echo -e "# PODNAME: $PODNAME\n\n" | sponge -a $DST
echo -e "## DO NOT EDIT. Generated from $SRC using $GENERATOR.\n" | sponge -a $DST
## Add code to $DST
jupyter nbconvert $SRC --to script --stdout | sponge -a $DST;
[Collect / OPERATORS]
command = operator
[Collect / DESTRUCTORS]
command = destruct
[Leftovers]
[Generic / SEE ALSO]
[Region / postlude]
[Authors]
[Legal]
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