AI-TensorFlow-Libtensorflow
view release on metacpan or search on metacpan
lib/AI/TensorFlow/Libtensorflow/Manual/Notebook/InferenceUsingTFHubMobileNetV2Model.pod view on Meta::CPAN
my @image_names = sort keys %images_for_test_to_uri;
if( IN_IPERL ) {
IPerl->html(
my_table( \@image_names, sub {
my ($image_name, $h) = @_;
(
$h->tt($image_name),
$h->a( { href => $images_for_test_to_uri{$image_name} },
$h->img({
src => $images_for_test_to_uri{$image_name},
alt => $image_name,
width => '50%',
})
),
)
})
);
}
sub imager_paste_center_pad {
my ($inner, $padded_sz, @rest) = @_;
my $outer = Imager->new( List::Util::mesh( [qw(xsize ysize)], $padded_sz ),
@rest
);
$outer->paste(
left => int( ($outer->getwidth - $inner->getwidth ) / 2 ),
top => int( ($outer->getheight - $inner->getheight) / 2 ),
src => $inner,
);
$outer;
}
sub imager_scale_to {
my ($img, $image_size) = @_;
my $rescaled = $img->scale(
List::Util::mesh( [qw(xpixels ypixels)], $image_size ),
type => 'min',
qtype => 'mixing', # 'mixing' seems to work better than 'normal'
);
}
sub load_image_to_pdl {
my ($uri, $image_size) = @_;
my $http = HTTP::Tiny->new;
my $response = $http->get( $uri );
die "Could not fetch image from $uri" unless $response->{success};
say "Downloaded $uri";
my $img = Imager->new;
$img->read( data => $response->{content} );
my $rescaled = imager_scale_to($img, $image_size);
say sprintf "Rescaled image from [ %d x %d ] to [ %d x %d ]",
$img->getwidth, $img->getheight,
$rescaled->getwidth, $rescaled->getheight;
my $padded = imager_paste_center_pad($rescaled, $image_size,
# ARGB fits in 32-bits (uint32_t)
channels => 4
);
say sprintf "Padded to [ %d x %d ]", $padded->getwidth, $padded->getheight;
# Create PDL ndarray from Imager data in-memory.
my $data;
$padded->write( data => \$data, type => 'raw' )
or die "could not write ". $padded->errstr;
# $data is packed as PDL->dims == [w,h] with ARGB pixels
# $ PDL::howbig(ulong) # 4
my $pdl_raw = zeros(ulong, $padded->getwidth, $padded->getheight);
${ $pdl_raw->get_dataref } = $data;
$pdl_raw->upd_data;
# Split uint32_t pixels into first dimension with 3 channels (R,G,B) with values 0-255.
my @shifts = map 8*$_, 0..2;
my $pdl_channels = $pdl_raw->dummy(0)
->and2(ulong(map 0xFF << $_, @shifts)->slice(':,*,*') )
->shiftright( ulong(@shifts)->slice(':,*,*') )
->byte;
my $pdl_scaled = (
# Scale to [ 0, 1 ].
( $pdl_channels / float(255) )
);
## flip vertically to see image right way up
#show_in_gnuplot( $pdl_channels->slice(':,:,-1:0') ); #DEBUG
#show_in_gnuplot( $pdl_scaled->slice(':,:,-1:0') * 255.0 ); #DEBUG
$pdl_scaled;
}
my @pdl_images = map {
load_image_to_pdl(
$images_for_test_to_uri{$_},
$model_name_to_params{$model_name}{image_size}
);
} @image_names;
my $pdl_image_batched = cat(@pdl_images);
my $t = FloatPDLTOTFTensor($pdl_image_batched);
p $pdl_image_batched;
p $t;
my $RunSession = sub {
my ($session, $t) = @_;
my @outputs_t;
$session->Run(
undef,
$outputs{in}, [$t],
$outputs{out}, \@outputs_t,
undef,
undef,
$s
);
AssertOK($s);
return $outputs_t[0];
};
lib/AI/TensorFlow/Libtensorflow/Manual/Notebook/InferenceUsingTFHubMobileNetV2Model.pod view on Meta::CPAN
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 {
my ($batch_idx, $h) = @_;
my $image_name = $image_names[$batch_idx];
my @top_for_image = $top_lists[$batch_idx]->list;
(
$h->tt($image_name),
$h->a( { href => $images_for_test_to_uri{$image_name} },
$h->img({
src => $images_for_test_to_uri{$image_name},
alt => $image_name,
width => '50%',
})
),
do {
my @tr;
push @tr, [ $h->th('Rank', 'Label No', 'Label', 'Prob') ];
while( my ($i, $label_index) = each @top_for_image ) {
my $class_index = $includes_background_class ? $label_index : $label_index + 1;
push @tr, [ $h->td(
$i + 1,
$class_index,
$labels[$class_index],
$probabilities_batched->at($label_index,$batch_idx),
) ];
}
$h->table([$h->tr(@tr)])
},
)
})
);
IPerl->display($html);
} else {
for my $batch_idx (0..$#image_names) {
my $image_name = $image_names[$batch_idx];
my @top_for_image = $top_lists[$batch_idx]->list;
my @td;
say "Image name: `$image_name`";
my $header = [ ('Rank', 'Label No', 'Label', 'Prob') ];
my @rows;
while( my ($i, $label_index) = each @top_for_image ) {
my $class_index = $includes_background_class ? $label_index : $label_index + 1;
push @rows, [ (
$i + 1,
$class_index,
$labels[$class_index],
$probabilities_batched->at($label_index,$batch_idx),
) ];
}
say generate_table( rows => [ $header, @rows ], header_row => 1 );
print "\n";
}
}
my $p_approx_batched = $probabilities_batched->sumover->approx(1, 1e-5);
p $p_approx_batched;
say "All probabilities sum up to approximately 1" if $p_approx_batched->all->sclr;
use Filesys::DiskUsage qw/du/;
my $total = du( { 'human-readable' => 1, dereference => 1 },
$model_archive_path, $model_base, $labels_path );
say "Disk space usage: $total"; undef;
my @solid_channel_uris = (
'https://upload.wikimedia.org/wikipedia/commons/thumb/6/62/Solid_red.svg/480px-Solid_red.svg.png',
'https://upload.wikimedia.org/wikipedia/commons/thumb/1/1d/Green_00FF00_9x9.svg/480px-Green_00FF00_9x9.svg.png',
'https://upload.wikimedia.org/wikipedia/commons/thumb/f/ff/Solid_blue.svg/480px-Solid_blue.svg.png',
);
undef;
__END__
=pod
=encoding UTF-8
=head1 NAME
AI::TensorFlow::Libtensorflow::Manual::Notebook::InferenceUsingTFHubMobileNetV2Model - Using TensorFlow to do image classification using a pre-trained model
=head1 SYNOPSIS
The following tutorial is based on the L<Image Classification with TensorFlow Hub notebook|https://github.com/tensorflow/docs/blob/master/site/en/hub/tutorials/image_classification.ipynb>. It uses a pre-trained model based on the I<MobileNet V2> arch...
Please look at the L<SECURITY note|https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md> regarding running models as models are programs. You can also used C<saved_model_cli scan> to check for L<security-sensitive "denylisted ops"|https:/...
If you would like to visualise a model, you can use L<Netron|https://github.com/lutzroeder/netron> on the C<.pb> file.
=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-...
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);
use Syntax::Construct qw(each-array);
lib/AI/TensorFlow/Libtensorflow/Manual/Notebook/InferenceUsingTFHubMobileNetV2Model.pod view on Meta::CPAN
$h->a( { href => $images_for_test_to_uri{$image_name} },
$h->img({
src => $images_for_test_to_uri{$image_name},
alt => $image_name,
width => '50%',
})
),
)
})
);
}
B<DISPLAY>:
=for html <span style="display:inline-block;margin-left:1em;"><p><table style="width: 100%"><tr><td><tt>apple</tt></td><td><a href="https://upload.wikimedia.org/wikipedia/commons/1/15/Red_Apple.jpg"><img alt="apple" src="https://upload.wikimedia.org/...
=head2 Download the test images and transform them into suitable input data
We now fetch these images and prepare them to be the in the needed format by using C<Imager> to resize and add padding. Then we turn the C<Imager> data into a C<PDL> ndarray. Since the C<Imager> data is stored as 32-bits with 4 channels in the order ...
We then take all the PDL ndarrays and concatenate them. Again, note that the dimension lists for the PDL ndarray and the TFTensor are reversed.
sub imager_paste_center_pad {
my ($inner, $padded_sz, @rest) = @_;
my $outer = Imager->new( List::Util::mesh( [qw(xsize ysize)], $padded_sz ),
@rest
);
$outer->paste(
left => int( ($outer->getwidth - $inner->getwidth ) / 2 ),
top => int( ($outer->getheight - $inner->getheight) / 2 ),
src => $inner,
);
$outer;
}
sub imager_scale_to {
my ($img, $image_size) = @_;
my $rescaled = $img->scale(
List::Util::mesh( [qw(xpixels ypixels)], $image_size ),
type => 'min',
qtype => 'mixing', # 'mixing' seems to work better than 'normal'
);
}
sub load_image_to_pdl {
my ($uri, $image_size) = @_;
my $http = HTTP::Tiny->new;
my $response = $http->get( $uri );
die "Could not fetch image from $uri" unless $response->{success};
say "Downloaded $uri";
my $img = Imager->new;
$img->read( data => $response->{content} );
my $rescaled = imager_scale_to($img, $image_size);
say sprintf "Rescaled image from [ %d x %d ] to [ %d x %d ]",
$img->getwidth, $img->getheight,
$rescaled->getwidth, $rescaled->getheight;
my $padded = imager_paste_center_pad($rescaled, $image_size,
# ARGB fits in 32-bits (uint32_t)
channels => 4
);
say sprintf "Padded to [ %d x %d ]", $padded->getwidth, $padded->getheight;
# Create PDL ndarray from Imager data in-memory.
my $data;
$padded->write( data => \$data, type => 'raw' )
or die "could not write ". $padded->errstr;
# $data is packed as PDL->dims == [w,h] with ARGB pixels
# $ PDL::howbig(ulong) # 4
my $pdl_raw = zeros(ulong, $padded->getwidth, $padded->getheight);
${ $pdl_raw->get_dataref } = $data;
$pdl_raw->upd_data;
# Split uint32_t pixels into first dimension with 3 channels (R,G,B) with values 0-255.
my @shifts = map 8*$_, 0..2;
my $pdl_channels = $pdl_raw->dummy(0)
->and2(ulong(map 0xFF << $_, @shifts)->slice(':,*,*') )
->shiftright( ulong(@shifts)->slice(':,*,*') )
->byte;
my $pdl_scaled = (
# Scale to [ 0, 1 ].
( $pdl_channels / float(255) )
);
## flip vertically to see image right way up
#show_in_gnuplot( $pdl_channels->slice(':,:,-1:0') ); #DEBUG
#show_in_gnuplot( $pdl_scaled->slice(':,:,-1:0') * 255.0 ); #DEBUG
$pdl_scaled;
}
my @pdl_images = map {
load_image_to_pdl(
$images_for_test_to_uri{$_},
$model_name_to_params{$model_name}{image_size}
);
} @image_names;
my $pdl_image_batched = cat(@pdl_images);
my $t = FloatPDLTOTFTensor($pdl_image_batched);
p $pdl_image_batched;
p $t;
B<STREAM (STDOUT)>:
Downloaded https://upload.wikimedia.org/wikipedia/commons/1/15/Red_Apple.jpg
Rescaled image from [ 2418 x 2192 ] to [ 224 x 203 ]
Padded to [ 224 x 224 ]
Downloaded https://upload.wikimedia.org/wikipedia/commons/1/1c/Bananas_white_background.jpg
Rescaled image from [ 1600 x 1067 ] to [ 224 x 149 ]
Padded to [ 224 x 224 ]
Downloaded https://upload.wikimedia.org/wikipedia/commons/6/63/LT_471_%28LTZ_1471%29_Arriva_London_New_Routemaster_%2819522859218%29.jpg
Rescaled image from [ 3840 x 2560 ] to [ 224 x 149 ]
Padded to [ 224 x 224 ]
Downloaded https://upload.wikimedia.org/wikipedia/commons/4/49/2013-2016_Toyota_Corolla_%28ZRE172R%29_SX_sedan_%282018-09-17%29_01.jpg
Rescaled image from [ 4152 x 2252 ] to [ 224 x 121 ]
Padded to [ 224 x 224 ]
Downloaded https://upload.wikimedia.org/wikipedia/commons/4/4d/Cat_November_2010-1a.jpg
Rescaled image from [ 1795 x 2397 ] to [ 168 x 224 ]
Padded to [ 224 x 224 ]
Downloaded https://upload.wikimedia.org/wikipedia/commons/archive/a/a9/20090914031557%21Saluki_dog_breed.jpg
Rescaled image from [ 543 x 523 ] to [ 224 x 216 ]
Padded to [ 224 x 224 ]
Downloaded https://upload.wikimedia.org/wikipedia/commons/b/b8/James_Flamingos_MC.jpg
Rescaled image from [ 3000 x 1999 ] to [ 224 x 149 ]
Padded to [ 224 x 224 ]
Downloaded https://upload.wikimedia.org/wikipedia/commons/f/f7/Honey_comb.jpg
Rescaled image from [ 800 x 600 ] to [ 224 x 168 ]
Padded to [ 224 x 224 ]
Downloaded https://upload.wikimedia.org/wikipedia/commons/d/da/Steinway_%26_Sons_upright_piano%2C_model_K-132%2C_manufactured_at_Steinway%27s_factory_in_Hamburg%2C_Germany.png
Rescaled image from [ 2059 x 2080 ] to [ 222 x 224 ]
Padded to [ 224 x 224 ]
Downloaded https://upload.wikimedia.org/wikipedia/commons/4/44/Black_tea_pot_cropped.jpg
Rescaled image from [ 900 x 838 ] to [ 224 x 209 ]
Padded to [ 224 x 224 ]
Downloaded https://upload.wikimedia.org/wikipedia/commons/b/b0/Bengal_tiger_%28Panthera_tigris_tigris%29_female_3_crop.jpg
Rescaled image from [ 4500 x 3000 ] to [ 224 x 149 ]
Padded to [ 224 x 224 ]
Downloaded https://upload.wikimedia.org/wikipedia/commons/8/80/Turtle_golfina_escobilla_oaxaca_mexico_claudio_giovenzana_2010.jpg
Rescaled image from [ 2000 x 1329 ] to [ 224 x 149 ]
Padded to [ 224 x 224 ]
B<STREAM (STDERR)>:
=for html <span style="display:inline-block;margin-left:1em;"><pre style="display: block"><code><span style="color: #cc66cc;">PDL</span><span style="color: #33ccff;"> {</span><span style="">
</span><span style="color: #6666cc;">Data </span><span style=""> : </span><span style="color: #669933;">too long to print</span><span style="">
</span><span style="color: #6666cc;">Type </span><span style=""> : </span><span style="color: #cc66cc;">float</span><span style="">
</span><span style="color: #6666cc;">Shape </span><span style=""> : </span><span style="color: #33ccff;">[</span><span style="color: #9999cc;">3 224 224 12</span><span style="color: #33ccff;">]</span><span style="">
</span><span style="color: #6666cc;">Nelem </span><span style=""> : </span><span style="color: #dd6;">1806336</span><span style="">
</span><span style="color: #6666cc;">Min </span><span style=""> : </span><span style="color: #f66;">0</span><span style="">
</span><span style="color: #6666cc;">Max </span><span style=""> : </span><span style="color: #99f;">1</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><span style="color: #cc66cc;">AI::TensorFlow::Libtensorflow::Tensor</span><span style=""> </span><span style="color: #33ccff;">{</span><span style="">
</span><span style="color: #6666cc;">Type </span><span style=""> </span><span style="color: #cc66cc;">FLOAT</span><span style="">
</span><span style="color: #6666cc;">Dims </span><span style=""> </span><span style="color: #33ccff;">[</span><span style=""> </span><span style="color: #ff6633;">12</span><span style=""> </span><span style="color: #ff6633;">224</span><...
</span><span style="color: #6666cc;">NumDims </span><span style=""> </span><span style="color: #ff6633;">4</span><span style="">
</span><span style="color: #6666cc;">ElementCount </span><span style=""> </span><span style="color: #ff6633;">1806336</span><span style="">
</span><span style="color: #33ccff;">}</span><span style="">
</span></code></pre></span>
=head2 Run the model for inference
We can use the C<Run> method to run the session and get the output C<TFTensor>.
First, we send a single random input to warm up the model.
my $RunSession = sub {
my ($session, $t) = @_;
my @outputs_t;
$session->Run(
undef,
$outputs{in}, [$t],
$outputs{out}, \@outputs_t,
undef,
undef,
$s
);
AssertOK($s);
return $outputs_t[0];
};
say "Warming up the model";
use PDL::GSL::RNG;
my $rng = PDL::GSL::RNG->new('default');
my $image_size = $model_name_to_params{$model_name}{image_size};
my $warmup_input = zeros(float, 3, @$image_size, 1 );
$rng->get_uniform($warmup_input);
p $RunSession->($session, FloatPDLTOTFTensor($warmup_input));
B<STREAM (STDOUT)>:
Warming up the model
B<STREAM (STDERR)>:
=for html <span style="display:inline-block;margin-left:1em;"><pre style="display: block"><code><span style="color: #cc66cc;">AI::TensorFlow::Libtensorflow::Tensor</span><span style=""> </span><span style="color: #33ccff;">{</span><span style="">
</span><span style="color: #6666cc;">Type </span><span style=""> </span><span style="color: #cc66cc;">FLOAT</span><span style="">
</span><span style="color: #6666cc;">Dims </span><span style=""> </span><span style="color: #33ccff;">[</span><span style=""> </span><span style="color: #ff6633;">1</span><span style=""> </span><span style="color: #ff6633;">1001</span><...
</span><span style="color: #6666cc;">NumDims </span><span style=""> </span><span style="color: #ff6633;">2</span><span style="">
</span><span style="color: #6666cc;">ElementCount </span><span style=""> </span><span style="color: #ff6633;">1001</span><span style="">
</span><span style="color: #33ccff;">}</span><span style="">
</span></code></pre></span>
Then we send the batched image data. The returned scores need to by normalised using the L<softmax function|https://en.wikipedia.org/wiki/Softmax_function> with the following formula (taken from Wikipedia):
$$ {\displaystyle \sigma (\mathbf {z} )I<{i}={\frac {e^{z>{i}}}{\sum I<{j=1}^{K}e^{z>{j}}}}\ \ {\text{ for }}i=1,\dotsc ,K{\text{ and }}\mathbf {z} =(zI<{1},\dotsc ,z>{K})\in \mathbb {R} ^{K}.} $$
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;
B<STREAM (STDERR)>:
=for html <span style="display:inline-block;margin-left:1em;"><pre style="display: block"><code><span style="color: #cc66cc;">PDL</span><span style="color: #33ccff;"> {</span><span style="">
</span><span style="color: #6666cc;">Data </span><span style=""> : </span><span style="color: #669933;">too long to print</span><span style="">
</span><span style="color: #6666cc;">Type </span><span style=""> : </span><span style="color: #cc66cc;">float</span><span style="">
</span><span style="color: #6666cc;">Shape </span><span style=""> : </span><span style="color: #33ccff;">[</span><span style="color: #9999cc;">1001 12</span><span style="color: #33ccff;">]</span><span style="">
</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 {
my ($batch_idx, $h) = @_;
my $image_name = $image_names[$batch_idx];
my @top_for_image = $top_lists[$batch_idx]->list;
(
$h->tt($image_name),
$h->a( { href => $images_for_test_to_uri{$image_name} },
$h->img({
src => $images_for_test_to_uri{$image_name},
alt => $image_name,
width => '50%',
})
),
do {
my @tr;
push @tr, [ $h->th('Rank', 'Label No', 'Label', 'Prob') ];
while( my ($i, $label_index) = each @top_for_image ) {
my $class_index = $includes_background_class ? $label_index : $label_index + 1;
push @tr, [ $h->td(
$i + 1,
$class_index,
$labels[$class_index],
$probabilities_batched->at($label_index,$batch_idx),
) ];
}
$h->table([$h->tr(@tr)])
},
)
})
);
IPerl->display($html);
} else {
for my $batch_idx (0..$#image_names) {
my $image_name = $image_names[$batch_idx];
my @top_for_image = $top_lists[$batch_idx]->list;
my @td;
say "Image name: `$image_name`";
my $header = [ ('Rank', 'Label No', 'Label', 'Prob') ];
my @rows;
while( my ($i, $label_index) = each @top_for_image ) {
my $class_index = $includes_background_class ? $label_index : $label_index + 1;
push @rows, [ (
$i + 1,
$class_index,
$labels[$class_index],
$probabilities_batched->at($label_index,$batch_idx),
) ];
}
say generate_table( rows => [ $header, @rows ], header_row => 1 );
print "\n";
}
}
B<DISPLAY>:
=for html <span style="display:inline-block;margin-left:1em;"><p><table style="width: 100%"><tr><td><tt>apple</tt></td><td><a href="https://upload.wikimedia.org/wikipedia/commons/1/15/Red_Apple.jpg"><img alt="apple" src="https://upload.wikimedia.org/...
my $p_approx_batched = $probabilities_batched->sumover->approx(1, 1e-5);
p $p_approx_batched;
say "All probabilities sum up to approximately 1" if $p_approx_batched->all->sclr;
B<STREAM (STDOUT)>:
All probabilities sum up to approximately 1
B<STREAM (STDERR)>:
=for html <span style="display:inline-block;margin-left:1em;"><pre style="display: block"><code><span style="color: #cc66cc;">PDL</span><span style="color: #33ccff;"> {</span><span style="">
</span><span style="color: #6666cc;">Data </span><span style=""> : </span><span style="color: #33ccff;">[</span><span style="color: #ff6633;">1 1 1 1 1 1 1 1 1 1 1 1</span><span style="color: #33ccff;">]</span><span style="">
</span><span style="color: #6666cc;">Type </span><span style=""> : </span><span style="color: #cc66cc;">double</span><span style="">
</span><span style="color: #6666cc;">Shape </span><span style=""> : </span><span style="color: #33ccff;">[</span><span style="color: #9999cc;">12</span><span style="color: #33ccff;">]</span><span style="">
</span><span style="color: #6666cc;">Nelem </span><span style=""> : </span><span style="color: #dd6;">12</span><span style="">
</span><span style="color: #6666cc;">Min </span><span style=""> : </span><span style="color: #f66;">1</span><span style="">
</span><span style="color: #6666cc;">Max </span><span style=""> : </span><span style="color: #99f;">1</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>
B<RESULT>:
1
=head1 RESOURCE USAGE
use Filesys::DiskUsage qw/du/;
my $total = du( { 'human-readable' => 1, dereference => 1 },
$model_archive_path, $model_base, $labels_path );
say "Disk space usage: $total"; undef;
B<STREAM (STDOUT)>:
Disk space usage: 27.45M
=head1 DEBUGGING
The following images can be used to test the C<load_image_to_pdl> function.
my @solid_channel_uris = (
'https://upload.wikimedia.org/wikipedia/commons/thumb/6/62/Solid_red.svg/480px-Solid_red.svg.png',
'https://upload.wikimedia.org/wikipedia/commons/thumb/1/1d/Green_00FF00_9x9.svg/480px-Green_00FF00_9x9.svg.png',
'https://upload.wikimedia.org/wikipedia/commons/thumb/f/ff/Solid_blue.svg/480px-Solid_blue.svg.png',
);
undef;
=head1 CPANFILE
requires 'AI::TensorFlow::Libtensorflow';
( run in 1.105 second using v1.01-cache-2.11-cpan-39bf76dae61 )