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lib/AI/TensorFlow/Libtensorflow/Manual/Notebook/InferenceUsingTFHubEnformerGeneExprPredModel.pod  view on Meta::CPAN

# PODNAME: AI::TensorFlow::Libtensorflow::Manual::Notebook::InferenceUsingTFHubEnformerGeneExprPredModel


## DO NOT EDIT. Generated from notebook/InferenceUsingTFHubEnformerGeneExprPredModel.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);
use Syntax::Construct qw( // );

use lib::projectroot qw(lib);

BEGIN {
    if( IN_IPERL ) {
        $ENV{TF_CPP_MIN_LOG_LEVEL} = 3;
    }
    require AI::TensorFlow::Libtensorflow;
}

use URI ();
use HTTP::Tiny ();
use Path::Tiny qw(path);

use File::Which ();

use List::Util ();

use Data::Printer ( output => 'stderr', return_value => 'void', filters => ['PDL'] );
use Data::Printer::Filter::PDL ();
use Text::Table::Tiny qw(generate_table);

my $s = AI::TensorFlow::Libtensorflow::Status->New;
sub AssertOK {
    die "Status $_[0]: " . $_[0]->Message
        unless $_[0]->GetCode == AI::TensorFlow::Libtensorflow::Status::OK;
    return;
}
AssertOK($s);

use PDL;
use AI::TensorFlow::Libtensorflow::DataType qw(FLOAT);

use FFI::Platypus::Memory qw(memcpy);
use FFI::Platypus::Buffer qw(scalar_to_pointer);

sub FloatPDLTOTFTensor {
    my ($p) = @_;
    return AI::TensorFlow::Libtensorflow::Tensor->New(
        FLOAT, [ reverse $p->dims ], $p->get_dataref, sub { undef $p }
    );
}

sub FloatTFTensorToPDL {
    my ($t) = @_;

    my $pdl = zeros(float,reverse( map $t->Dim($_), 0..$t->NumDims-1 ) );

    memcpy scalar_to_pointer( ${$pdl->get_dataref} ),
        scalar_to_pointer( ${$t->Data} ),
        $t->ByteSize;
    $pdl->upd_data;

    $pdl;
}

# Model handle
my $model_uri = URI->new( 'https://tfhub.dev/deepmind/enformer/1' );
$model_uri->query_form( 'tf-hub-format' => 'compressed' );
my $model_base = substr( $model_uri->path, 1 ) =~ s,/,_,gr;
my $model_archive_path = "${model_base}.tar.gz";
my $model_sequence_length = 393_216; # bp

lib/AI/TensorFlow/Libtensorflow/Manual/Notebook/InferenceUsingTFHubEnformerGeneExprPredModel.pod  view on Meta::CPAN

=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 *

the Docker image takes 3 GB or more of disk space;

=item *

the model and data takes 5 GB or more of disk space.

=back

meaning that you will need a total of B<8 GB> of disk space. You may need at least B<4 GB> of free memory to run the model.

=head1 COLOPHON

The following document is either a POD file which can additionally be run as a Perl script or a Jupyter Notebook which can be run in L<IPerl|https://p3rl.org/Devel::IPerl> (viewable online at L<nbviewer|https://nbviewer.org/github/EntropyOrg/perl-AI-...

You will also need the executables C<gunzip>, C<bgzip>, and C<samtools>. Furthermore,

=over

=item *

C<Bio::DB::HTS> requires C<libhts> and

=item *

C<PDL::Graphics::Gnuplot> requires C<gnuplot>.

=back

If you are running the code, you may optionally install the L<C<tensorflow> Python package|https://www.tensorflow.org/install/pip> in order to access the C<saved_model_cli> command, but this is only used for informational purposes.

=head1 TUTORIAL

=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);
  use Syntax::Construct qw( // );
  
  use lib::projectroot qw(lib);
  
  BEGIN {
      if( IN_IPERL ) {
          $ENV{TF_CPP_MIN_LOG_LEVEL} = 3;
      }
      require AI::TensorFlow::Libtensorflow;
  }
  
  use URI ();
  use HTTP::Tiny ();
  use Path::Tiny qw(path);
  
  use File::Which ();
  
  use List::Util ();
  
  use Data::Printer ( output => 'stderr', return_value => 'void', filters => ['PDL'] );
  use Data::Printer::Filter::PDL ();
  use Text::Table::Tiny qw(generate_table);
  
  my $s = AI::TensorFlow::Libtensorflow::Status->New;
  sub AssertOK {
      die "Status $_[0]: " . $_[0]->Message
          unless $_[0]->GetCode == AI::TensorFlow::Libtensorflow::Status::OK;
      return;
  }
  AssertOK($s);

And create helpers for converting between C<PDL> ndarrays and C<TFTensor> ndarrays.

  use PDL;
  use AI::TensorFlow::Libtensorflow::DataType qw(FLOAT);
  
  use FFI::Platypus::Memory qw(memcpy);
  use FFI::Platypus::Buffer qw(scalar_to_pointer);
  
  sub FloatPDLTOTFTensor {
      my ($p) = @_;
      return AI::TensorFlow::Libtensorflow::Tensor->New(
          FLOAT, [ reverse $p->dims ], $p->get_dataref, sub { undef $p }
      );
  }
  
  sub FloatTFTensorToPDL {
      my ($t) = @_;
  
      my $pdl = zeros(float,reverse( map $t->Dim($_), 0..$t->NumDims-1 ) );
  
      memcpy scalar_to_pointer( ${$pdl->get_dataref} ),
          scalar_to_pointer( ${$t->Data} ),
          $t->ByteSize;
      $pdl->upd_data;
  
      $pdl;
  }

=head2 Download model and data

=over