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

  my $tftensor_output_by_name = $RunSession->($session, $t);
  
  my %pdl_output_by_name = map {
      $_ => FloatTFTensorToPDL( $tftensor_output_by_name->{$_} )
  } keys $tftensor_output_by_name->%*;
  
  undef;

=head2 Results summary

Then we use a score threshold to select the objects of interest.

  my $min_score_thresh = 0.30;
  
  my $which_detect = which( $pdl_output_by_name{detection_scores} > $min_score_thresh );
  
  my %subset;
  
  $subset{detection_boxes}   = $pdl_output_by_name{detection_boxes}->dice('X', $which_detect);
  $subset{detection_classes} = $pdl_output_by_name{detection_classes}->dice($which_detect);
  $subset{detection_scores}  = $pdl_output_by_name{detection_scores}->dice($which_detect);

lib/AI/TensorFlow/Libtensorflow/Manual/Notebook/InferenceUsingTFHubMobileNetV2Model.pod  view on Meta::CPAN

my $warmup_input = zeros(float, 3, @$image_size, 1 );
$rng->get_uniform($warmup_input);

p $RunSession->($session, FloatPDLTOTFTensor($warmup_input));

my $output_pdl_batched = FloatTFTensorToPDL($RunSession->($session, $t));
my $softmax = sub { ( map $_/sumover($_)->dummy(0), exp($_[0]) )[0] };
my $probabilities_batched = $softmax->($output_pdl_batched);
p $probabilities_batched;

my $N = 5; # number to select

my $top_batched = $probabilities_batched->qsorti->slice([-1, -$N]);

my @top_lists   = dog($top_batched);

my $includes_background_class = $probabilities_batched->dim(0) == IMAGENET_LABEL_COUNT_WITH_BG;

if( IN_IPERL ) {
    my $html = IPerl->html(
        my_table( [0..$#image_names], sub {

lib/AI/TensorFlow/Libtensorflow/Manual/Notebook/InferenceUsingTFHubMobileNetV2Model.pod  view on Meta::CPAN

    </span><span style="color: #6666cc;">Nelem   </span><span style=""> : </span><span style="color: #dd6;">12012</span><span style="">
    </span><span style="color: #6666cc;">Min     </span><span style=""> : </span><span style="color: #f66;">2.73727380317723e-07</span><span style="">
    </span><span style="color: #6666cc;">Max     </span><span style=""> : </span><span style="color: #99f;">0.980696022510529</span><span style="">
    </span><span style="color: #6666cc;">Badflag </span><span style=""> : </span><span style="color: #2c2;">No</span><span style="">
    </span><span style="color: #6666cc;">Has Bads</span><span style=""> : </span><span style="color: #2c2;">No</span><span style="">
</span><span style="color: #33ccff;">}</span><span style="">
</span></code></pre></span>

=head2 Results summary

Then select the top 5 of those and find their class labels.

  my $N = 5; # number to select
  
  my $top_batched = $probabilities_batched->qsorti->slice([-1, -$N]);
  
  my @top_lists   = dog($top_batched);
  
  my $includes_background_class = $probabilities_batched->dim(0) == IMAGENET_LABEL_COUNT_WITH_BG;
  
  if( IN_IPERL ) {
      my $html = IPerl->html(
          my_table( [0..$#image_names], sub {