AI-MXNet-Gluon-ModelZoo

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examples/image_classification.pl  view on Meta::CPAN

$image = mx->image->imread($image);
$image = mx->image->resize_short($image, $model =~ /inception/ ? 330 : 256);
($image) = mx->image->center_crop($image, [($model =~ /inception/ ? 299 : 224)x2]);

## CV that is used to read image is column major (as PDL)
$image = $image->transpose([2,0,1])->expand_dims(axis=>0);

## normalizing the image
my $rgb_mean = nd->array([0.485, 0.456, 0.406])->reshape([1,3,1,1]);
my $rgb_std = nd->array([0.229, 0.224, 0.225])->reshape([1,3,1,1]);
$image = ($image->astype('float32') / 255 - $rgb_mean) / $rgb_std;

# Now we can recognize the object in the image.
# We perform an additional softmax on the output to obtain probability scores.
# And then print the top-5 recognized objects.
my $prob = $net->($image)->softmax;
for my $idx (@{ $prob->topk(k=>5)->at(0) })
{
    my $i = $idx->asscalar;
    printf(
        "With prob = %.5f, it contains %s\n",

lib/AI/MXNet/Gluon/ModelZoo/Vision/ResNet.pm  view on Meta::CPAN

}

method hybrid_forward(GluonClass $F, GluonInput $x)
{
    my $residual = $x;
    $x = $self->body->($x);
    if(defined $self->downsample)
    {
        $residual = $self->downsample->($residual);
    }
    $x = $F->Activation($residual+$x, act_type=>'relu');
    return $x;
}

package AI::MXNet::Gluon::ModelZoo::Vision::ResNet::BottleneckV1;
use AI::MXNet::Gluon::Mouse;
extends 'AI::MXNet::Gluon::HybridBlock';

=head1 NAME

    AI::MXNet::Gluon::ModelZoo::Vision::ResNet::BottleneckV1 - Bottleneck V1 from "Deep Residual Learning for Image Recognition"

lib/AI/MXNet/Gluon/ModelZoo/Vision/ResNet.pm  view on Meta::CPAN

}

method hybrid_forward(GluonClass $F, GluonInput $x)
{
    my $residual = $x;
    $x = $self->body->($x);
    if(defined $self->downsample)
    {
        $residual = $self->downsample->($residual);
    }
    $x = $F->Activation($residual+$x, act_type=>'relu');
    return $x;
}

package AI::MXNet::Gluon::ModelZoo::Vision::ResNet::BasicBlockV2;
use AI::MXNet::Gluon::Mouse;
extends 'AI::MXNet::Gluon::HybridBlock';

=head1 NAME 

    AI::MXNet::Gluon::ModelZoo::Vision::ResNet::BasicBlockV2 - BasicBlock V2 from "Identity Mappings in Deep Residual Networks"

lib/AI/MXNet/Gluon/ModelZoo/Vision/ResNet.pm  view on Meta::CPAN

    else
    {
        $self->downsample(undef);
    }
}

method hybrid_forward(GluonClass $F, GluonInput $x)
{
    my $residual = $x;
    $x = $self->bn1->($x);
    $x = $F->Activation($x, act_type=>'relu');
    if(defined $self->downsample)
    {
        $residual = $self->downsample->($x);
    }
    $x = $self->conv1->($x);

    $x = $self->bn2->($x);
    $x = $F->Activation($x, act_type=>'relu');
    $x = $self->conv2->($x);

    return $x + $residual;
}


package AI::MXNet::Gluon::ModelZoo::Vision::ResNet::BottleneckV2;
use AI::MXNet::Gluon::Mouse;
extends 'AI::MXNet::Gluon::HybridBlock';

lib/AI/MXNet/Gluon/ModelZoo/Vision/ResNet.pm  view on Meta::CPAN

    else
    {
        $self->downsample(undef);
    }
}

method hybrid_forward(GluonClass $F, GluonInput $x)
{
    my $residual = $x;
    $x = $self->bn1->($x);
    $x = $F->Activation($x, act_type=>'relu');
    if(defined $self->downsample)
    {
        $residual = $self->downsample->($x);
    }
    $x = $self->conv1->($x);

    $x = $self->bn2->($x);
    $x = $F->Activation($x, act_type=>'relu');
    $x = $self->conv2->($x);

    $x = $self->bn3->($x);
    $x = $F->Activation($x, act_type=>'relu');
    $x = $self->conv3->($x);

    return $x + $residual;
}


# Nets
package AI::MXNet::Gluon::ModelZoo::Vision::ResNet::V1;
use AI::MXNet::Gluon::Mouse;
extends 'AI::MXNet::Gluon::HybridBlock';

lib/AI/MXNet/Gluon/ModelZoo/Vision/ResNet.pm  view on Meta::CPAN

# Constructor
method get_resnet(
    Int $version, Int $num_layers, Bool :$pretrained=0,
    AI::MXNet::Context :$ctx=AI::MXNet::Context->cpu(),
    Str :$root='~/.mxnet/models',
    Maybe[Int]  :$classes=,
    Maybe[Bool] :$thumbnail=
)
{
    my ($block_type, $layers, $channels) = @{ $resnet_spec{$num_layers} };
    my $resnet_class = $resnet_net_versions[$version-1];
    confess("invalid resnet $version [$version], can be 1,2") unless $resnet_class;
    my $block_class = $resnet_block_versions[$version-1]{$block_type};
    my $net = $resnet_class->new(
        $block_class, $layers, $channels,
        (defined($classes) ? (classes => $classes) : ()),
        (defined($thumbnail) ? (thumbnail => $thumbnail) : ())
    );
    if($pretrained)
    {
        $net->load_parameters(
            AI::MXNet::Gluon::ModelZoo::ModelStore->get_model_file(
                "resnet${num_layers}_v$version",

lib/AI/MXNet/Gluon/ModelZoo/Vision/VGG.pm  view on Meta::CPAN

    my $featurizer = nn->HybridSequential(prefix=>'');
    for(enumerate($self->layers))
    {
        my ($i, $num) = @$_;
        for(0..$num-1)
        {
            $featurizer->add(
                nn->Conv2D(
                    $self->filters->[$i], kernel_size => 3, padding => 1,
                    weight_initializer => mx->init->Xavier(
                        rnd_type    => 'gaussian',
                        factor_type => 'out',
                        magnitude   => 2
                    ),
                    bias_initializer=>'zeros'
                )
            );
            if($self->batch_norm)
            {
                $featurizer->add(nn->BatchNorm());
            }
            $featurizer->add(nn->Activation('relu'));