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

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


## 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;
}

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

use File::Which ();

use List::Util 1.56 qw(mesh);

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

use Imager;

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 UINT8);

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;
}

sub Uint8PDLTOTFTensor {
    my ($p) = @_;
    return AI::TensorFlow::Libtensorflow::Tensor->New(
        UINT8, [ reverse $p->dims ], $p->get_dataref, sub { undef $p }
    );
}

sub Uint8TFTensorToPDL {
    my ($t) = @_;

    my $pdl = zeros(byte,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;
}

# image_size => [width, height] (but usually square images)

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

=pod

=encoding UTF-8

=head1 NAME

AI::TensorFlow::Libtensorflow::Manual::Notebook::InferenceUsingTFHubCenterNetObjDetect - Using TensorFlow to do object detection using a pre-trained model

=head1 SYNOPSIS

The following tutorial is based on the L<TensorFlow Hub Object Detection Colab notebook|https://www.tensorflow.org/hub/tutorials/tf2_object_detection>. It uses a pre-trained model based on the I<CenterNet> architecture trained on the I<COCO 2017> dat...

Some of this code is identical to that of C<InferenceUsingTFHubMobileNetV2Model> notebook. Please look there for an 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.

=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-...

=over

=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 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;
  }
  
  use URI ();
  use HTTP::Tiny ();
  use Path::Tiny qw(path);
  
  use File::Which ();
  
  use List::Util 1.56 qw(mesh);
  
  use Data::Printer ( output => 'stderr', return_value => 'void', filters => ['PDL'] );
  use Data::Printer::Filter::PDL ();
  use Text::Table::Tiny qw(generate_table);
  
  use Imager;
  
  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 UINT8);
  
  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;
  }
  
  sub Uint8PDLTOTFTensor {
      my ($p) = @_;
      return AI::TensorFlow::Libtensorflow::Tensor->New(
          UINT8, [ reverse $p->dims ], $p->get_dataref, sub { undef $p }
      );
  }
  
  sub Uint8TFTensorToPDL {
      my ($t) = @_;
  
      my $pdl = zeros(byte,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;
  }