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

  my $label_count = List::Util::max keys %labels_map;
  
  say "We have a label count of $label_count. These labels include: ",
      join ", ", List::Util::head( 5, @labels_map{ sort keys %labels_map } );

=head2 Load the model and session

We define the tag set C<[ 'serve' ]> which we will use to load the model.

  my @tags = ( 'serve' );

We can examine what computations are contained in the graph in terms of the names of the inputs and outputs of an operation found in the graph by running C<saved_model_cli>.

  if( File::Which::which('saved_model_cli')) {
      local $ENV{TF_CPP_MIN_LOG_LEVEL} = 3; # quiet the TensorFlow logger for the following command
      system(qw(saved_model_cli show),
          qw(--dir)           => $model_base,
          qw(--tag_set)       => join(',', @tags),
          qw(--signature_def) => 'serving_default'
      ) == 0 or die "Could not run saved_model_cli";
  } else {
      say "Install the tensorflow Python package to get the `saved_model_cli` command.";
  }

The above C<saved_model_cli> output shows that the model input is at C<serving_default_input_tensor:0> which means the operation named C<serving_default_input_tensor> at index C<0> and there are multiple outputs with different shapes.

Per the L<model description|https://tfhub.dev/tensorflow/centernet/hourglass_512x512/1> on TensorFlow Hub:

=over 2

B<Inputs>

A three-channel image of variable size - the model does NOT support batching. The input tensor is a C<tf.uint8> tensor with shape [1, height, width, 3] with values in [0, 255].

B<Outputs>

The output dictionary contains:

=over

=item -

C<num_detections>: a C<tf.int> tensor with only one value, the number of detections [N].

=item -

C<detection_boxes>: a C<tf.float32> tensor of shape [N, 4] containing bounding box coordinates in the following order: [ymin, xmin, ymax, xmax].

=item -

C<detection_classes>: a C<tf.int> tensor of shape [N] containing detection class index from the label file.

=item -

C<detection_scores>: a C<tf.float32> tensor of shape [N] containing detection scores.

=back

=back

Note that the above documentation has two errors: both C<num_detections> and C<detection_classes> are not of type C<tf.int>, but are actually C<tf.float32>.

Now we can load the model from that folder with the tag set C<[ 'serve' ]> by using the C<LoadFromSavedModel> constructor to create a C<::Graph> and a C<::Session> for that graph.

  my $opt = AI::TensorFlow::Libtensorflow::SessionOptions->New;
  
  my $graph = AI::TensorFlow::Libtensorflow::Graph->New;
  my $session = AI::TensorFlow::Libtensorflow::Session->LoadFromSavedModel(
      $opt, undef, $model_base, \@tags, $graph, undef, $s
  );
  AssertOK($s);

So let's use the names from the C<saved_model_cli> output to create our C<::Output> C<ArrayRef>s.

  my %ops = (
      in  => {
          op   =>  $graph->OperationByName('serving_default_input_tensor'),
          dict => {
              input_tensor => 0,
          }
      },
      out => {
          op => $graph->OperationByName('StatefulPartitionedCall'),
          dict => {
              detection_boxes   => 0,
              detection_classes => 1,
              detection_scores  => 2,
              num_detections    => 3,
          }
      },
  );
  
  my %outputs;
  
  %outputs = map {
      my $put_type = $_;
      my $op = $ops{$put_type}{op};
      my $port_dict = $ops{$put_type}{dict};
  
     $put_type => +{
          map {
              my $dict_key = $_;
              my $index = $port_dict->{$_};
              $dict_key => AI::TensorFlow::Libtensorflow::Output->New( {
                  oper => $op,
                  index => $index,
              });
          } keys %$port_dict
       }
  } keys %ops;
  
  p %outputs;

Now we can get the following testing image from GitHub.

  use HTML::Tiny;
  
  my %images_for_test_to_uri = (
      "beach_scene" => 'https://github.com/tensorflow/models/blob/master/research/object_detection/test_images/image2.jpg?raw=true',
  );