AI-MXNet-Gluon-ModelZoo
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# "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::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;
}
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