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

        $out = $F->elemwise_add($out, $x);
    }
    return $out;
}

package AI::MXNet::Gluon::ModelZoo::Vision::MobileNet;
use AI::MXNet::Gluon::Mouse;
use AI::MXNet::Base;
extends 'AI::MXNet::Gluon::HybridBlock';
has 'multiplier' => (is => 'ro', isa => 'Num', default => 1);
has 'classes'    => (is => 'ro', isa => 'Int', default => 1000);
method python_constructor_arguments(){ [qw/multiplier classes/] }

=head1 NAME

    AI::MXNet::Gluon::ModelZoo::Vision::MobileNet - MobileNet model from the
        "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"
=cut

=head1 DESCRIPTION

    MobileNet model from the
    "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"
    <https://arxiv.org/abs/1704.04861> paper.

    Parameters
    ----------
    multiplier : Num, default 1.0
        The width multiplier for controling the model size. Only multipliers that are no
        less than 0.25 are supported. The actual number of channels is equal to the original
        channel size multiplied by this multiplier.
    classes : Int, default 1000
        Number of classes for the output layer.
=cut

func _add_conv(
    $out, :$channels=1, :$kernel=1, :$stride=1, :$pad=0,
    :$num_group=1, :$active=1, :$relu6=0
)
{
    $out->add(nn->Conv2D($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'));
    }
}


func _add_conv_dw($out, :$dw_channels=, :$channels=, :$stride=, :$relu6=0)
{
    _add_conv($out, channels=>$dw_channels, kernel=>3, stride=>$stride,
              pad=>1, num_group=>$dw_channels, relu6=>$relu6);
    _add_conv($out, channels=>$channels, relu6=>$relu6);
}

sub BUILD
{
    my $self = shift;
    $self->name_scope(sub {
        $self->features(nn->HybridSequential(prefix=>''));
        $self->features->name_scope(sub {
            _add_conv($self->features, channels=>int(32 * $self->multiplier), kernel=>3, pad=>1, stride=>2);
            my $dw_channels = [map { int($_ * $self->multiplier) } (32, 64, (128)x2, (256)x2, (512)x6, 1024)];
            my $channels = [map { int($_ * $self->multiplier) } (64, (128)x2, (256)x2, (512)x6, (1024)x2)];
            my $strides = [(1, 2)x3, (1)x5, 2, 1];
            for(zip($dw_channels, $channels, $strides))
            {
                my ($dwc, $c, $s) = @$_;
                _add_conv_dw($self->features, dw_channels=>$dwc, channels=>$c, stride=>$s);
            }
            $self->features->add(nn->GlobalAvgPool2D());
            $self->features->add(nn->Flatten());
        });
        $self->output(nn->Dense($self->classes));
    });
}

method hybrid_forward(GluonClass $F, GluonInput $x)
{
    $x = $self->features->($x);
    $x = $self->output->($x);
    return $x;
}

package AI::MXNet::Gluon::ModelZoo::Vision::MobileNetV2;
use AI::MXNet::Gluon::Mouse;
use AI::MXNet::Base;
extends 'AI::MXNet::Gluon::HybridBlock';
has 'multiplier' => (is => 'ro', isa => 'Num', default => 1);
has 'classes'    => (is => 'ro', isa => 'Int', default => 1000);
method python_constructor_arguments(){ [qw/multiplier classes/] }

=head1 NAME

    AI::MXNet::Gluon::ModelZoo::Vision::MobileNetV2 - MobileNet model from the
        "Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation"
=cut

=head1 DESCRIPTION

    MobileNetV2 model from the
    "Inverted Residuals and Linear Bottlenecks:
      Mobile Networks for Classification, Detection and Segmentation"
    <https://arxiv.org/abs/1801.04381> paper.

    Parameters
    ----------
    multiplier : Num, default 1.0
        The width multiplier for controling the model size. Only multipliers that are no
        less than 0.25 are supported. The actual number of channels is equal to the original
        channel size multiplied by this multiplier.
    classes : Int, default 1000
        Number of classes for the output layer.
=cut

func _add_conv(
    $out, $channels, :$kernel=1, :$stride=1, :$pad=0,
    :$num_group=1, :$active=1, :$relu6=0
)
{
    $out->add(nn->Conv2D($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->name_scope(sub {
        $self->features(nn->HybridSequential(prefix=>'features_'));
        $self->features->name_scope(sub {
            _add_conv(
                $self->features, int(32 * $self->multiplier), kernel=>3,
                stride=>2, pad=>1, relu6=>1
            );

            my $in_channels_group = [map { int($_ * $self->multiplier) } (32, 16, (24)x2, (32)x3, (64)x4, (96)x3, (160)x3)];
            my $channels_group = [map { int($_ * $self->multiplier) } (16, (24)x2, (32)x3, (64)x4, (96)x3, (160)x3, 320)];
            my $ts = [1, (6)x16];
            my $strides = [(1, 2)x2, 1, 1, 2, (1)x6, 2, (1)x3];

            for(zip($in_channels_group, $channels_group, $ts, $strides))
            {
                my ($in_c, $c, $t, $s) = @$_;
                $self->features->add(
                    AI::MXNet::Gluon::ModelZoo::Vision::MobileNet::LinearBottleneck->new(
                        in_channels=>$in_c, channels=>$c,
                        t=>$t, stride=>$s
                    )
                );
            }

            my $last_channels = $self->multiplier > 1 ? int(1280 * $self->multiplier) : 1280;
            _add_conv($self->features, $last_channels, relu6=>1);
            $self->features->add(nn->GlobalAvgPool2D());
        });

        $self->output(nn->HybridSequential(prefix=>'output_'));
        $self->output->name_scope(sub {
            $self->output->add(
                nn->Conv2D($self->classes, 1, use_bias=>0, prefix=>'pred_'),
                nn->Flatten()
            );
        });
    });
}

method hybrid_forward(GluonClass $F, GluonInput $x)
{
    $x = $self->features->($x);
    $x = $self->output->($x);
    return $x;
}

package AI::MXNet::Gluon::ModelZoo::Vision;

=head2 get_mobilenet

    MobileNet model from the
    "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"
    <https://arxiv.org/abs/1704.04861> paper.

    Parameters
    ----------
    $multiplier : Num
        The width multiplier for controling the model size. Only multipliers that are no
        less than 0.25 are supported. The actual number of channels is equal to the original
        channel size multiplied by this multiplier.
    :$pretrained : Bool, default 0
        Whether to load the pretrained weights for model.
    :$ctx : AI::MXNet::Context, default CPU
        The context in which to load the pretrained weights.
    :$root : Str, default '~/.mxnet/models'
        Location for keeping the model parameters.
=cut

method get_mobilenet(
    Num $multiplier, Bool :$pretrained=0, AI::MXNet::Context :$ctx=AI::MXNet::Context->cpu(),
    Str :$root='~/.mxnet/models'
)
{
    my $net = AI::MXNet::Gluon::ModelZoo::Vision::MobileNet->new($multiplier);
    if($pretrained)
    {
        my $version_suffix = sprintf("%.2f", $multiplier);
        if($version_suffix eq '1.00' or $version_suffix eq '0.50')
        {
            $version_suffix =~ s/.$//;
        }
        $net->load_parameters(
            AI::MXNet::Gluon::ModelZoo::ModelStore->get_model_file(
                "mobilenet$version_suffix",
                root=>$root
            ),
            ctx=>$ctx
        );
    }
    return $net;
}

=head2 get_mobilenet_v2

    MobileNetV2 model from the
    "Inverted Residuals and Linear Bottlenecks:
      Mobile Networks for Classification, Detection and Segmentation"
    <https://arxiv.org/abs/1801.04381> paper.

    Parameters
    ----------
    $multiplier : Num