AI-TensorFlow-Libtensorflow
view release on metacpan or search on metacpan
lib/AI/TensorFlow/Libtensorflow/Manual/Notebook/InferenceUsingTFHubCenterNetObjDetect.pod view on Meta::CPAN
$subset{detection_class_labels}[$idx],
100*$subset{detection_scores}->at($idx,0) ) =>
at => $label_xy, 'left',
offset => 'character 0,-0.25',
qq{font ",12" boxed front tc rgb "#ffffff"} ], ],
)
} 0..$subset{detection_boxes}->dim(1)-1
);
$gp->plot(
topcmds => q{set style textbox opaque fc "#505050f0" noborder},
square => 1,
yrange => [$pdl_images[0]->dim(2),0],
with => 'image', $pdl_images[0],
);
$gp->close;
IPerl->png( bytestream => path($plot_output_path)->slurp_raw ) if IN_IPERL;
use Filesys::DiskUsage qw/du/;
lib/AI/TensorFlow/Libtensorflow/Manual/Notebook/InferenceUsingTFHubCenterNetObjDetect.pod view on Meta::CPAN
$subset{detection_class_labels}[$idx],
100*$subset{detection_scores}->at($idx,0) ) =>
at => $label_xy, 'left',
offset => 'character 0,-0.25',
qq{font ",12" boxed front tc rgb "#ffffff"} ], ],
)
} 0..$subset{detection_boxes}->dim(1)-1
);
$gp->plot(
topcmds => q{set style textbox opaque fc "#505050f0" noborder},
square => 1,
yrange => [$pdl_images[0]->dim(2),0],
with => 'image', $pdl_images[0],
);
$gp->close;
IPerl->png( bytestream => path($plot_output_path)->slurp_raw ) if IN_IPERL;
=head1 RESOURCE USAGE
lib/AI/TensorFlow/Libtensorflow/Manual/Notebook/InferenceUsingTFHubEnformerGeneExprPredModel.pod view on Meta::CPAN
AI::TensorFlow::Libtensorflow::Output->New({
oper => $graph->OperationByName('StatefulPartitionedCall'),
index => 1,
}),
);
p %puts;
B<STREAM (STDERR)>:
=for html <span style="display:inline-block;margin-left:1em;"><pre style="display: block"><code><span style="color: #33ccff;">{</span><span style="">
</span><span style="color: #6666cc;">inputs_args_0</span><span style="color: #33ccff;"> </span><span style="color: #cc66cc;">AI::TensorFlow::Libtensorflow::Output</span><span style=""> </span><span style="color: #33ccff;">{</span><span style=""...
</span><span style="color: #6666cc;">index</span><span style="color: #33ccff;"> </span><span style="color: #ff6633;">0</span><span style="color: #33ccff;">,</span><span style="">
</span><span style="color: #6666cc;">oper</span><span style=""> </span><span style="color: #33ccff;"> </span><span style="color: #cc66cc;">AI::TensorFlow::Libtensorflow::Operation</span><span style=""> </span><span style="color: #33ccff;">{...
</span><span style="color: #6666cc;">Name</span><span style=""> </span><span style="color: #33ccff;"> </span><span style="color: #33ccff;">"</span><span style="color: #669933;">serving_default_args_0</span><span style="color: ...
</span><span style="color: #6666cc;">NumInputs</span><span style=""> </span><span style="color: #33ccff;"> </span><span style="color: #ff6633;">0</span><span style="color: #33ccff;">,</span><span style="">
</span><span style="color: #6666cc;">NumOutputs</span><span style="color: #33ccff;"> </span><span style="color: #ff6633;">1</span><span style="color: #33ccff;">,</span><span style="">
</span><span style="color: #6666cc;">OpType</span><span style=""> </span><span style="color: #33ccff;"> </span><span style="color: #33ccff;">"</span><span style="color: #669933;">Placeholder</span><span style="color: #33ccff;">&...
</span><span style="color: #33ccff;">}</span><span style="">
</span><span style="color: #33ccff;">}</span><span style="color: #33ccff;">,</span><span style="">
</span><span style="color: #6666cc;">outputs_human</span><span style="color: #33ccff;"> </span><span style="color: #cc66cc;">AI::TensorFlow::Libtensorflow::Output</span><span style=""> </span><span style="color: #33ccff;">{</span><span style=""...
</span><span style="color: #6666cc;">index</span><span style="color: #33ccff;"> </span><span style="color: #ff6633;">0</span><span style="color: #33ccff;">,</span><span style="">
</span><span style="color: #6666cc;">oper</span><span style=""> </span><span style="color: #33ccff;"> </span><span style="color: #cc66cc;">AI::TensorFlow::Libtensorflow::Operation</span><span style=""> </span><span style="color: #33ccff;">{...
</span><span style="color: #6666cc;">Name</span><span style=""> </span><span style="color: #33ccff;"> </span><span style="color: #33ccff;">"</span><span style="color: #669933;">StatefulPartitionedCall</span><span style="color:...
</span><span style="color: #6666cc;">NumInputs</span><span style=""> </span><span style="color: #33ccff;"> </span><span style="color: #ff6633;">274</span><span style="color: #33ccff;">,</span><span style="">
</span><span style="color: #6666cc;">NumOutputs</span><span style="color: #33ccff;"> </span><span style="color: #ff6633;">2</span><span style="color: #33ccff;">,</span><span style="">
</span><span style="color: #6666cc;">OpType</span><span style=""> </span><span style="color: #33ccff;"> </span><span style="color: #33ccff;">"</span><span style="color: #669933;">StatefulPartitionedCall</span><span style="color:...
</span><span style="color: #33ccff;">}</span><span style="">
</span><span style="color: #33ccff;">}</span><span style="color: #33ccff;">,</span><span style="">
</span><span style="color: #6666cc;">outputs_mouse</span><span style="color: #33ccff;"> </span><span style="color: #cc66cc;">AI::TensorFlow::Libtensorflow::Output</span><span style=""> </span><span style="color: #33ccff;">{</span><span style=""...
</span><span style="color: #6666cc;">index</span><span style="color: #33ccff;"> </span><span style="color: #ff6633;">1</span><span style="color: #33ccff;">,</span><span style="">
</span><span style="color: #6666cc;">oper</span><span style=""> </span><span style="color: #33ccff;"> </span><span style="color: #cc66cc;">AI::TensorFlow::Libtensorflow::Operation</span><span style=""> </span><span style="color: #33ccff;">{...
</span><span style="color: #6666cc;">Name</span><span style=""> </span><span style="color: #33ccff;"> </span><span style="color: #33ccff;">"</span><span style="color: #669933;">StatefulPartitionedCall</span><span style="color:...
</span><span style="color: #6666cc;">NumInputs</span><span style=""> </span><span style="color: #33ccff;"> </span><span style="color: #ff6633;">274</span><span style="color: #33ccff;">,</span><span style="">
</span><span style="color: #6666cc;">NumOutputs</span><span style="color: #33ccff;"> </span><span style="color: #ff6633;">2</span><span style="color: #33ccff;">,</span><span style="">
</span><span style="color: #6666cc;">OpType</span><span style=""> </span><span style="color: #33ccff;"> </span><span style="color: #33ccff;">"</span><span style="color: #669933;">StatefulPartitionedCall</span><span style="color:...
</span><span style="color: #33ccff;">}</span><span style="">
</span><span style="color: #33ccff;">}</span><span style="">
</span><span style="color: #33ccff;">}</span><span style="">
</span></code></pre></span>
We need a helper to simplify running the session and getting just the predictions that we want.
my $predict_on_batch = sub {
my ($session, $t) = @_;
my @outputs_t;
$session->Run(
undef,
lib/AI/TensorFlow/Libtensorflow/Manual/Notebook/InferenceUsingTFHubEnformerGeneExprPredModel.pod view on Meta::CPAN
p $predictions;
$t->mark('END');
$t->report();
B<STREAM (STDERR)>:
=begin 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;">896</span><s...
</span><span style="color: #6666cc;">NumDims </span><span style=""> </span><span style="color: #ff6633;">3</span><span style="">
</span><span style="color: #6666cc;">ElementCount </span><span style=""> </span><span style="color: #ff6633;">4760448</span><span style="">
</span><span style="color: #33ccff;">}</span><span style="">
Devel::Timer Report -- Total time: 14.5641 secs
Interval Time Percent
----------------------------------------------
01 -> 02 14.5634 100.00% prediction of sequence -> End of prediction of sequence
02 -> 03 0.0007 0.00% End of prediction of sequence -> END
00 -> 01 0.0000 0.00% INIT -> prediction of sequence
</span></code></pre></span>
lib/AI/TensorFlow/Libtensorflow/Manual/Notebook/InferenceUsingTFHubEnformerGeneExprPredModel.pod view on Meta::CPAN
$gp->end_multi;
$gp->close;
if( IN_IPERL ) {
IPerl->png( bytestream => path($plot_output_path)->slurp_raw );
}
B<DISPLAY>:
=for html <span style="display:inline-block;margin-left:1em;"><p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA+gAAAMgCAIAAAA/et9qAAAgAElEQVR4nOzdd2AUVeIH8Ddb0jshBAIEpSo1GjoIpyAgCOqd3uGdoGBBUQQFRUVBRbkTf9gOBQucqFiwUhSSgJQYCCSBkJBAet1k...
=head2 Parts of the original notebook that fall outside the scope
In the orignal notebook, there are several more steps that have not been ported here:
=over
=item 1.
"Compute contribution scores":
lib/AI/TensorFlow/Libtensorflow/Manual/Notebook/InferenceUsingTFHubMobileNetV2Model.pod view on Meta::CPAN
$pdl->upd_data;
$pdl;
}
use HTML::Tiny;
sub my_table {
my ($data, $cb) = @_;
my $h = HTML::Tiny->new;
$h->table( { style => 'width: 100%' },
[
$h->tr(
map {
[
$h->td( $cb->($_, $h) )
]
} @$data
)
]
)
lib/AI/TensorFlow/Libtensorflow/Manual/Notebook/InferenceUsingTFHubMobileNetV2Model.pod view on Meta::CPAN
$pdl;
}
The following is just a small helper to generate an HTML C<<< <table> >>> for output in C<IPerl>.
use HTML::Tiny;
sub my_table {
my ($data, $cb) = @_;
my $h = HTML::Tiny->new;
$h->table( { style => 'width: 100%' },
[
$h->tr(
map {
[
$h->td( $cb->($_, $h) )
]
} @$data
)
]
)
lib/AI/TensorFlow/Libtensorflow/Manual/Notebook/InferenceUsingTFHubMobileNetV2Model.pod view on Meta::CPAN
say "Input: " , $outputs{in}[0];
say "Output: ", $outputs{out}[0];
B<STREAM (STDOUT)>:
Input: serving_default_inputs:0
Output: StatefulPartitionedCall:0
B<STREAM (STDERR)>:
=for html <span style="display:inline-block;margin-left:1em;"><pre style="display: block"><code><span style="color: #33ccff;">{</span><span style="">
</span><span style="color: #6666cc;">in</span><span style=""> </span><span style="color: #33ccff;"> </span><span style="color: #33ccff;">[</span><span style="">
</span><span style="color: #9999cc;">[0] </span><span style="color: #cc66cc;">AI::TensorFlow::Libtensorflow::Output</span><span style=""> </span><span style="color: #33ccff;">{</span><span style="">
</span><span style="color: #6666cc;">index</span><span style="color: #33ccff;"> </span><span style="color: #ff6633;">0</span><span style="color: #33ccff;">,</span><span style="">
</span><span style="color: #6666cc;">oper</span><span style=""> </span><span style="color: #33ccff;"> </span><span style="color: #cc66cc;">AI::TensorFlow::Libtensorflow::Operation</span><span style=""> </span><span style="color: #33...
</span><span style="color: #6666cc;">Name</span><span style=""> </span><span style="color: #33ccff;"> </span><span style="color: #33ccff;">"</span><span style="color: #669933;">serving_default_inputs</span><span style=...
</span><span style="color: #6666cc;">NumInputs</span><span style=""> </span><span style="color: #33ccff;"> </span><span style="color: #ff6633;">0</span><span style="color: #33ccff;">,</span><span style="">
</span><span style="color: #6666cc;">NumOutputs</span><span style="color: #33ccff;"> </span><span style="color: #ff6633;">1</span><span style="color: #33ccff;">,</span><span style="">
</span><span style="color: #6666cc;">OpType</span><span style=""> </span><span style="color: #33ccff;"> </span><span style="color: #33ccff;">"</span><span style="color: #669933;">Placeholder</span><span style="color: #33...
</span><span style="color: #33ccff;">}</span><span style="">
</span><span style="color: #33ccff;">}</span><span style="">
</span><span style="color: #33ccff;">]</span><span style="color: #33ccff;">,</span><span style="">
</span><span style="color: #6666cc;">out</span><span style="color: #33ccff;"> </span><span style="color: #33ccff;">[</span><span style="">
</span><span style="color: #9999cc;">[0] </span><span style="color: #cc66cc;">AI::TensorFlow::Libtensorflow::Output</span><span style=""> </span><span style="color: #33ccff;">{</span><span style="">
</span><span style="color: #6666cc;">index</span><span style="color: #33ccff;"> </span><span style="color: #ff6633;">0</span><span style="color: #33ccff;">,</span><span style="">
</span><span style="color: #6666cc;">oper</span><span style=""> </span><span style="color: #33ccff;"> </span><span style="color: #cc66cc;">AI::TensorFlow::Libtensorflow::Operation</span><span style=""> </span><span style="color: #33...
</span><span style="color: #6666cc;">Name</span><span style=""> </span><span style="color: #33ccff;"> </span><span style="color: #33ccff;">"</span><span style="color: #669933;">StatefulPartitionedCall</span><span style...
</span><span style="color: #6666cc;">NumInputs</span><span style=""> </span><span style="color: #33ccff;"> </span><span style="color: #ff6633;">263</span><span style="color: #33ccff;">,</span><span style="">
</span><span style="color: #6666cc;">NumOutputs</span><span style="color: #33ccff;"> </span><span style="color: #ff6633;">1</span><span style="color: #33ccff;">,</span><span style="">
</span><span style="color: #6666cc;">OpType</span><span style=""> </span><span style="color: #33ccff;"> </span><span style="color: #33ccff;">"</span><span style="color: #669933;">StatefulPartitionedCall</span><span style...
</span><span style="color: #33ccff;">}</span><span style="">
</span><span style="color: #33ccff;">}</span><span style="">
</span><span style="color: #33ccff;">]</span><span style="">
</span><span style="color: #33ccff;">}</span><span style="">
</span></code></pre></span>
B<RESULT>:
1
Now we can get the following testing images from Wikimedia.
my %images_for_test_to_uri = (
"tiger" => "https://upload.wikimedia.org/wikipedia/commons/b/b0/Bengal_tiger_%28Panthera_tigris_tigris%29_female_3_crop.jpg",
lib/AI/TensorFlow/Libtensorflow/Manual/Notebook/InferenceUsingTFHubMobileNetV2Model.pod view on Meta::CPAN
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) = @_;
lib/AI/TensorFlow/Libtensorflow/Manual/Notebook/InferenceUsingTFHubMobileNetV2Model.pod view on Meta::CPAN
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) = @_;
lib/AI/TensorFlow/Libtensorflow/Manual/Notebook/InferenceUsingTFHubMobileNetV2Model.pod view on Meta::CPAN
$rng->get_uniform($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]);
lib/AI/TensorFlow/Libtensorflow/Manual/Notebook/InferenceUsingTFHubMobileNetV2Model.pod view on Meta::CPAN
$probabilities_batched->at($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/;
( run in 0.454 second using v1.01-cache-2.11-cpan-49f99fa48dc )