AI-MXNet-Gluon-ModelZoo
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use strict;
use warnings;
use AI::MXNet::Function::Parameters;
package AI::MXNet::Gluon::ModelZoo::Vision::MobileNet::RELU6;
use AI::MXNet::Gluon::Mouse;
extends 'AI::MXNet::Gluon::HybridBlock';
method hybrid_forward(GluonClass $F, GluonInput $x)
{
return $F->clip($x, a_min => 0, a_max => 6, name=>"relu6");
}
package AI::MXNet::Gluon::ModelZoo::Vision::MobileNet::LinearBottleneck;
use AI::MXNet::Gluon::Mouse;
extends 'AI::MXNet::Gluon::HybridBlock';
has [qw/in_channels channels t stride/] => (is => 'ro', isa => 'Int', required => 1);
method python_constructor_arguments(){ [qw/in_channels channels t stride/] }
=head1 NAME
AI::MXNet::Gluon::ModelZoo::Vision::MobileNet::LinearBottleneck - LinearBottleneck used in MobileNetV2 model
=cut
=head1 DESCRIPTION
LinearBottleneck used in MobileNetV2 model from the
"Inverted Residuals and Linear Bottlenecks:
Mobile Networks for Classification, Detection and Segmentation"
<https://arxiv.org/abs/1801.04381> paper.
Parameters
----------
in_channels : Int
Number of input channels.
channels : Int
Number of output channels.
t : Int
Layer expansion ratio.
stride : Int
stride
=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;
$self->use_shortcut($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, $self->channels, active=>0, relu6=>1);
});
}
method hybrid_forward($F, $x)
{
my $out = $self->out->($x);
if($self->use_shortcut)
{
$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
----------
$multiplier : Num
The width multiplier for controling the model size. Only multipliers that are no
less than 0.25 are supported. The actual number of channels is equal to the original
channel size multiplied by this multiplier.
:$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_v2(
Num $multiplier, Bool :$pretrained=0, AI::MXNet::Context :$ctx=AI::MXNet::Context->cpu(),
Str :$root='~/.mxnet/models'
)
{
my $net = AI::MXNet::Gluon::ModelZoo::Vision::MobileNetV2->new($multiplier);
if($pretrained)
{
my $version_suffix = sprintf("%.2f", $multiplier);
if($version_suffix eq '1.00' or $version_suffix eq '0.50')
{
$version_suffix =~ s/.$//;
}
$net->load_parameters(
AI::MXNet::Gluon::ModelZoo::ModelStore->get_model_file(
"mobilenetv2_$version_suffix",
root=>$root
),
ctx=>$ctx
);
}
return $net;
}
=head2 mobilenet1_0
MobileNet model from the
"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"
<https://arxiv.org/abs/1704.04861> paper, with width multiplier 1.0.
Parameters
----------
:$pretrained : Bool, default 0
Whether to load the pretrained weights for model.
:$ctx : AI::MXNet::Context, default CPU
The context in which to load the pretrained weights.
=cut
method mobilenet1_0(%kwargs)
{
return __PACKAGE__->get_mobilenet(1.0, %kwargs);
}
=head2 mobilenet_v2_1_0
MobileNetV2 model from the
"Inverted Residuals and Linear Bottlenecks:
Mobile Networks for Classification, Detection and Segmentation"
<https://arxiv.org/abs/1801.04381> paper.
Parameters
----------
:$pretrained : Bool, default 0
Whether to load the pretrained weights for model.
:$ctx : AI::MXNet::Context, default CPU
The context in which to load the pretrained weights.
=cut
method mobilenet_v2_1_0(%kwargs)
{
return __PACKAGE__->get_mobilenet_v2(1.0, %kwargs);
}
=head2 mobilenet0_75
MobileNet model from the
"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"
<https://arxiv.org/abs/1704.04861> paper, with width multiplier 0.75.
Parameters
----------
:$pretrained : Bool, default 0
Whether to load the pretrained weights for model.
:$ctx : AI::MXNet::Context, default CPU
The context in which to load the pretrained weights.
=cut
method mobilenet0_75(%kwargs)
{
return __PACKAGE__->get_mobilenet(0.75, %kwargs);
}
=head2 mobilenet_v2_0_75
MobileNetV2 model from the
"Inverted Residuals and Linear Bottlenecks:
Mobile Networks for Classification, Detection and Segmentation"
<https://arxiv.org/abs/1801.04381> paper.
Parameters
----------
:$pretrained : Bool, default 0
Whether to load the pretrained weights for model.
:$ctx : AI::MXNet::Context, default CPU
The context in which to load the pretrained weights.
=cut
method mobilenet_v2_0_75(%kwargs)
{
return __PACKAGE__->get_mobilenet_v2(0.75, %kwargs);
}
=head2 mobilenet0_5
MobileNet model from the
"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"
<https://arxiv.org/abs/1704.04861> paper, with width multiplier 0.5.
Parameters
----------
:$pretrained : Bool, default 0
Whether to load the pretrained weights for model.
:$ctx : AI::MXNet::Context, default CPU
The context in which to load the pretrained weights.
=cut
method mobilenet0_5(%kwargs)
{
return __PACKAGE__->get_mobilenet(0.5, %kwargs);
}
=head2 mobilenet_v2_0_5
MobileNetV2 model from the
"Inverted Residuals and Linear Bottlenecks:
Mobile Networks for Classification, Detection and Segmentation"
<https://arxiv.org/abs/1801.04381> paper.
Parameters
----------
:$pretrained : Bool, default 0
Whether to load the pretrained weights for model.
:$ctx : AI::MXNet::Context, default CPU
The context in which to load the pretrained weights.
=cut
method mobilenet_v2_0_5(%kwargs)
{
return __PACKAGE__->get_mobilenet_v2(0.5, %kwargs);
}
=head2 mobilenet0_25
MobileNet model from the
"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"
<https://arxiv.org/abs/1704.04861> paper, with width multiplier 0.25.
Parameters
----------
:$pretrained : Bool, default 0
Whether to load the pretrained weights for model.
:$ctx : AI::MXNet::Context, default CPU
The context in which to load the pretrained weights.
=cut
method mobilenet0_25(%kwargs)
{
return __PACKAGE__->get_mobilenet(0.25, %kwargs);
}
=head2 mobilenet_v2_0_25
MobileNetV2 model from the
"Inverted Residuals and Linear Bottlenecks:
Mobile Networks for Classification, Detection and Segmentation"
<https://arxiv.org/abs/1801.04381> paper.
Parameters
----------
:$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.
=cut
method mobilenet_v2_0_25(%kwargs)
{
return __PACKAGE__->get_mobilenet_v2(0.25, %kwargs);
}
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