AI-MXNet-Gluon-ModelZoo
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examples/image_classification.pl view on Meta::CPAN
use warnings;
use AI::MXNet::Gluon::ModelZoo 'get_model';
use AI::MXNet::Gluon::Utils 'download';
use Getopt::Long qw(HelpMessage);
GetOptions(
## my Pembroke Welsh Corgi Kyuubi, enjoing Solar eclipse of August 21, 2017
'image=s' => \(my $image = 'http://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/'.
'gluon/dataset/kyuubi.jpg'),
'model=s' => \(my $model = 'resnet152_v2'),
'help' => sub { HelpMessage(0) },
) or HelpMessage(1);
## get a pretrained model (download parameters file if necessary)
my $net = get_model($model, pretrained => 1);
## ImageNet classes
my $fname = download('http://data.mxnet.io/models/imagenet/synset.txt');
my @text_labels = map { chomp; s/^\S+\s+//; $_ } IO::File->new($fname)->getlines;
## get the image from the disk or net
lib/AI/MXNet/Gluon/ModelZoo.pm view on Meta::CPAN
The context in which to load the pretrained weights.
:$root : Str, default '~/.mxnet/models'
Location for keeping the model parameters.
Returns
-------
HybridBlock
The model.
=cut
sub get_model
{
if(exists $models{lc $_[1]})
{
shift;
}
my ($name, %kwargs) = @_;
$name = lc $name;
Carp::confess(
"Model $name is not present in the zoo\nValid models are:\n".
join(', ', sort keys %models)."\n"
) unless exists $models{$name};
my $sub = $models{$name};
AI::MXNet::Gluon::ModelZoo::Vision->$sub(%kwargs);
}
sub vision { 'AI::MXNet::Gluon::ModelZoo::Vision' }
1;
=head1 AUTHOR
Sergey Kolychev, <sergeykolychev.github@gmail.com>
=head1 COPYRIGHT & LICENSE
This library is licensed under Apache 2.0 license L<https://www.apache.org/licenses/LICENSE-2.0>
lib/AI/MXNet/Gluon/ModelZoo/Vision.pm view on Meta::CPAN
use warnings;
use AI::MXNet::Gluon::ModelZoo::ModelStore;
use AI::MXNet::Gluon::ModelZoo::Vision::AlexNet;
use AI::MXNet::Gluon::ModelZoo::Vision::DenseNet;
use AI::MXNet::Gluon::ModelZoo::Vision::Inception;
use AI::MXNet::Gluon::ModelZoo::Vision::MobileNet;
use AI::MXNet::Gluon::ModelZoo::Vision::ResNet;
use AI::MXNet::Gluon::ModelZoo::Vision::SqueezeNet;
use AI::MXNet::Gluon::ModelZoo::Vision::VGG;
sub import
{
my ($class, $short_name) = @_;
if($short_name)
{
$short_name =~ s/[^\w:]//g;
if(length $short_name)
{
my $short_name_package =<<"EOP";
package $short_name;
\@${short_name}::ISA = ('AI::MXNet::Gluon::ModelZoo::Vision');
lib/AI/MXNet/Gluon/ModelZoo/Vision/AlexNet.pm view on Meta::CPAN
AlexNet model from the "One weird trick..." <https://arxiv.org/abs/1404.5997> paper.
Parameters
----------
classes : Int, default 1000
Number of classes for the output layer.
=cut
has 'classes' => (is => 'ro', isa => 'Int', default => 1000);
method python_constructor_arguments() { ['classes'] }
sub BUILD
{
my $self = shift;
$self->name_scope(sub {
$self->features(nn->HybridSequential(prefix=>''));
$self->features->name_scope(sub {
$self->features->add(nn->Conv2D(64, kernel_size=>11, strides=>4,
padding=>2, activation=>'relu'));
$self->features->add(nn->MaxPool2D(pool_size=>3, strides=>2));
$self->features->add(nn->Conv2D(192, kernel_size=>5, padding=>2,
activation=>'relu'));
$self->features->add(nn->MaxPool2D(pool_size=>3, strides=>2));
$self->features->add(nn->Conv2D(384, kernel_size=>3, padding=>1,
activation=>'relu'));
$self->features->add(nn->Conv2D(256, kernel_size=>3, padding=>1,
activation=>'relu'));
lib/AI/MXNet/Gluon/ModelZoo/Vision/DenseNet.pm view on Meta::CPAN
use strict;
use warnings;
use AI::MXNet::Base;
use AI::MXNet::Function::Parameters;
use AI::MXNet::Gluon::Mouse;
extends 'AI::MXNet::Gluon::HybridBlock';
func _make_dense_block($num_layers, $bn_size, $growth_rate, $dropout, $stage_index)
{
my $out = nn->HybridSequential(prefix=>"stage${stage_index}_");
$out->name_scope(sub {
for(1..$num_layers)
{
$out->add(_make_dense_layer($growth_rate, $bn_size, $dropout));
}
});
return $out;
}
func _make_dense_layer($growth_rate, $bn_size, $dropout)
{
lib/AI/MXNet/Gluon/ModelZoo/Vision/MobileNet.pm view on Meta::CPAN
)
{
$out->add(nn->Conv2D($channels, $kernel, $stride, $pad, groups=>$num_group, use_bias=>0));
$out->add(nn->BatchNorm(scale=>1));
if($active)
{
$out->add($relu6 ? AI::MXNet::Gluon::ModelZoo::Vision::MobileNet::RELU6->new : nn->Activation('relu'));
}
}
sub BUILD
{
my $self = shift;
$self->use_shortcut($self->stride == 1 and $self->in_channels == $self->channels);
$self->name_scope(sub {
$self->out(nn->HybridSequential());
_add_conv($self->out, $self->in_channels * $self->t, relu6=>1);
_add_conv(
$self->out, $self->in_channels * $self->t, kernel=>3, stride=>$self->stride,
pad=>1, num_group=>$self->in_channels * $self->t, relu6=>1
);
_add_conv($self->out, $self->channels, active=>0, relu6=>1);
});
}
lib/AI/MXNet/Gluon/ModelZoo/Vision/VGG.pm view on Meta::CPAN
Number of classification classes.
batch_norm : Bool, default 0
Use batch normalization.
=cut
method python_constructor_arguments() { [qw/layers filters classes batch_norm/] }
has ['layers',
'filters'] => (is => 'ro', isa => 'ArrayRef[Int]', required => 1);
has 'classes' => (is => 'ro', isa => 'Int', default => 1000);
has 'batch_norm' => (is => 'ro', isa => 'Bool', default => 0);
sub BUILD
{
my $self = shift;
assert(@{ $self->layers } == @{ $self->filters });
$self->name_scope(sub {
$self->features($self->_make_features());
$self->features->add(nn->Dense(4096, activation=>'relu',
weight_initializer=>'normal',
bias_initializer=>'zeros'));
$self->features->add(nn->Dropout(rate=>0.5));
$self->features->add(nn->Dense(4096, activation=>'relu',
weight_initializer=>'normal',
bias_initializer=>'zeros'));
$self->features->add(nn->Dropout(rate=>0.5));
$self->output(nn->Dense($self->classes,
t/test_gluon_model_zoo.t view on Meta::CPAN
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
use strict;
use warnings;
use AI::MXNet::Gluon::ModelZoo qw(get_model);
use Test::More tests => 34;
sub test_models
{
my @all_models = ('resnet34_v1', 'resnet18_v1', 'resnet50_v1', 'resnet101_v1', 'resnet152_v1',
'resnet18_v2', 'resnet34_v2', 'resnet50_v2', 'resnet101_v2', 'resnet152_v2',
'vgg11', 'vgg13', 'vgg16', 'vgg19',
'vgg11_bn', 'vgg13_bn', 'vgg16_bn', 'vgg19_bn',
'alexnet', 'inceptionv3',
'densenet121', 'densenet161', 'densenet169', 'densenet201',
'squeezenet1.0', 'squeezenet1.1',
'mobilenet1.0', 'mobilenet0.75', 'mobilenet0.5', 'mobilenet0.25',
'mobilenetv2_1.0', 'mobilenetv2_0.75', 'mobilenetv2_0.5', 'mobilenetv2_0.25');
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