AI-MXNet-Gluon-ModelZoo
view release on metacpan or search on metacpan
},
"name" : "AI-MXNet-Gluon-ModelZoo",
"no_index" : {
"directory" : [
"t",
"inc"
]
},
"prereqs" : {
"build" : {
"requires" : {}
},
"configure" : {
"requires" : {
"ExtUtils::MakeMaker" : "6.30"
}
},
"runtime" : {
"requires" : {
"AI::MXNet" : "1.31",
"AI::MXNet::Gluon::Contrib" : "1.3",
"IO::Uncompress::Unzip" : "0"
}
},
"test" : {
"requires" : {}
}
},
"release_status" : "stable",
"version" : "1.33"
}
---
abstract: 'Perl interface to MXNet Gluon ModelZoo'
author:
- 'Sergey Kolychev <sergeykolychev.github@gmail.com>'
build_requires: {}
configure_requires:
ExtUtils::MakeMaker: '6.30'
dynamic_config: 0
generated_by: 'ExtUtils::MakeMaker version 7.24, CPAN::Meta::Converter version 2.143240'
license: apache
meta-spec:
url: http://module-build.sourceforge.net/META-spec-v1.4.html
version: '1.4'
name: AI-MXNet-Gluon-ModelZoo
no_index:
directory:
- t
- inc
requires:
AI::MXNet: '1.31'
AI::MXNet::Gluon::Contrib: '1.3'
IO::Uncompress::Unzip: '0'
version: '1.33'
Makefile.PL view on Meta::CPAN
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "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 5.014000;
use ExtUtils::MakeMaker 6.30;
Makefile.PL view on Meta::CPAN
"CONFIGURE_REQUIRES" => {
"ExtUtils::MakeMaker" => "6.30"
},
"DISTNAME" => "AI-MXNet-Gluon-ModelZoo",
"EXE_FILES" => [],
"LICENSE" => "apache_2_0",
"NAME" => "AI::MXNet::Gluon::ModelZoo",
"PREREQ_PM" => {
"AI::MXNet" => "1.31",
"AI::MXNet::Gluon::Contrib" => "1.3",
"IO::Uncompress::Unzip" => "0"
},
"TEST_REQUIRES" => {},
"VERSION" => "1.33",
"test" => {
"TESTS" => "t/*.t"
}
);
my %FallbackPrereqs = (
"AI::MXNet" => "1.31",
"AI::MXNet::Gluon::Contrib" => "1.3",
"IO::Uncompress::Unzip" => "0"
);
unless ( eval { ExtUtils::MakeMaker->VERSION(6.63_03) } ) {
delete $WriteMakefileArgs{TEST_REQUIRES};
delete $WriteMakefileArgs{BUILD_REQUIRES};
$WriteMakefileArgs{PREREQ_PM} = \%FallbackPrereqs;
}
delete $WriteMakefileArgs{CONFIGURE_REQUIRES}
examples/image_classification.pl view on Meta::CPAN
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "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 '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;
examples/image_classification.pl view on Meta::CPAN
}
$image = $image =~ /^https?/ ? download($image) : $image;
# Following the conventional way of preprocessing ImageNet data:
# Resize the short edge into 256 pixes,
# And then perform a center crop to obtain a 224-by-224 image.
# The following code uses the image processing functions provided
# in the AI::MXNet::Image module.
$image = mx->image->imread($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",
$prob->at(0)->at($i)->asscalar, $text_labels[$i]
);
}
lib/AI/MXNet/Gluon/ModelZoo.pm view on Meta::CPAN
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "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.
package AI::MXNet::Gluon::ModelZoo;
use strict;
use warnings;
use AI::MXNet qw(mx);
use AI::MXNet::Gluon qw(gluon);
use AI::MXNet::Gluon::NN qw(nn);
use AI::MXNet::Gluon::Contrib;
lib/AI/MXNet/Gluon/ModelZoo.pm view on Meta::CPAN
=head1 DESCRIPTION
This module houses a collection of pretrained models (the parameters are hosted on public mxnet servers).
https://mxnet.incubator.apache.org/api/python/gluon/model_zoo.html
See examples/image_classification.pl for the example of real time image classification
using a pretrained model from the ModelZoo
=cut
our %models = qw/
resnet18_v1 resnet18_v1
resnet34_v1 resnet34_v1
resnet50_v1 resnet50_v1
resnet101_v1 resnet101_v1
resnet152_v1 resnet152_v1
resnet18_v2 resnet18_v2
resnet34_v2 resnet34_v2
resnet50_v2 resnet50_v2
resnet101_v2 resnet101_v2
resnet152_v2 resnet152_v2
vgg11 vgg11
vgg13 vgg13
vgg16 vgg16
vgg19 vgg19
vgg11_bn vgg11_bn
vgg13_bn vgg13_bn
vgg16_bn vgg16_bn
vgg19_bn vgg19_bn
alexnet alexnet
densenet121 densenet121
lib/AI/MXNet/Gluon/ModelZoo.pm view on Meta::CPAN
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;
lib/AI/MXNet/Gluon/ModelZoo/ModelStore.pm view on Meta::CPAN
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "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.
package AI::MXNet::Gluon::ModelZoo::ModelStore;
use strict;
use warnings;
use AI::MXNet::Function::Parameters;
=head1 NAME
AI::MXNet::Gluon::ModelZoo::ModelStore - Model zoo for pre-trained models.
=cut
use AI::MXNet::Gluon::Utils qw(download check_sha1);
use IO::Uncompress::Unzip qw(unzip);
use File::Path qw(make_path);
my %_model_sha1 = map { $_->[1] => $_->[0] } (
['44335d1f0046b328243b32a26a4fbd62d9057b45', 'alexnet'],
['f27dbf2dbd5ce9a80b102d89c7483342cd33cb31', 'densenet121'],
['b6c8a95717e3e761bd88d145f4d0a214aaa515dc', 'densenet161'],
['2603f878403c6aa5a71a124c4a3307143d6820e9', 'densenet169'],
['1cdbc116bc3a1b65832b18cf53e1cb8e7da017eb', 'densenet201'],
['ed47ec45a937b656fcc94dabde85495bbef5ba1f', 'inceptionv3'],
['9f83e440996887baf91a6aff1cccc1c903a64274', 'mobilenet0.25'],
['8e9d539cc66aa5efa71c4b6af983b936ab8701c3', 'mobilenet0.5'],
['529b2c7f4934e6cb851155b22c96c9ab0a7c4dc2', 'mobilenet0.75'],
['6b8c5106c730e8750bcd82ceb75220a3351157cd', 'mobilenet1.0'],
['36da4ff1867abccd32b29592d79fc753bca5a215', 'mobilenetv2_1.0'],
['e2be7b72a79fe4a750d1dd415afedf01c3ea818d', 'mobilenetv2_0.75'],
['aabd26cd335379fcb72ae6c8fac45a70eab11785', 'mobilenetv2_0.5'],
['ae8f9392789b04822cbb1d98c27283fc5f8aa0a7', 'mobilenetv2_0.25'],
['a0666292f0a30ff61f857b0b66efc0228eb6a54b', 'resnet18_v1'],
['48216ba99a8b1005d75c0f3a0c422301a0473233', 'resnet34_v1'],
['0aee57f96768c0a2d5b23a6ec91eb08dfb0a45ce', 'resnet50_v1'],
['d988c13d6159779e907140a638c56f229634cb02', 'resnet101_v1'],
['671c637a14387ab9e2654eafd0d493d86b1c8579', 'resnet152_v1'],
['a81db45fd7b7a2d12ab97cd88ef0a5ac48b8f657', 'resnet18_v2'],
['9d6b80bbc35169de6b6edecffdd6047c56fdd322', 'resnet34_v2'],
['ecdde35339c1aadbec4f547857078e734a76fb49', 'resnet50_v2'],
['18e93e4f48947e002547f50eabbcc9c83e516aa6', 'resnet101_v2'],
['f2695542de38cf7e71ed58f02893d82bb409415e', 'resnet152_v2'],
['264ba4970a0cc87a4f15c96e25246a1307caf523', 'squeezenet1.0'],
['33ba0f93753c83d86e1eb397f38a667eaf2e9376', 'squeezenet1.1'],
['dd221b160977f36a53f464cb54648d227c707a05', 'vgg11'],
['ee79a8098a91fbe05b7a973fed2017a6117723a8', 'vgg11_bn'],
['6bc5de58a05a5e2e7f493e2d75a580d83efde38c', 'vgg13'],
['7d97a06c3c7a1aecc88b6e7385c2b373a249e95e', 'vgg13_bn'],
['e660d4569ccb679ec68f1fd3cce07a387252a90a', 'vgg16'],
['7f01cf050d357127a73826045c245041b0df7363', 'vgg16_bn'],
['ad2f660d101905472b83590b59708b71ea22b2e5', 'vgg19'],
['f360b758e856f1074a85abd5fd873ed1d98297c3', 'vgg19_bn']
lib/AI/MXNet/Gluon/ModelZoo/Vision.pm view on Meta::CPAN
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "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.
package AI::MXNet::Gluon::ModelZoo::Vision;
use strict;
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;
lib/AI/MXNet/Gluon/ModelZoo/Vision/AlexNet.pm view on Meta::CPAN
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "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.
package AI::MXNet::Gluon::ModelZoo::Vision::AlexNet;
use strict;
use warnings;
use AI::MXNet::Function::Parameters;
use AI::MXNet::Gluon::Mouse;
extends 'AI::MXNet::Gluon::HybridBlock';
lib/AI/MXNet/Gluon/ModelZoo/Vision/AlexNet.pm view on Meta::CPAN
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'));
$self->features->add(nn->Conv2D(256, kernel_size=>3, padding=>1,
activation=>'relu'));
$self->features->add(nn->MaxPool2D(pool_size=>3, strides=>2));
$self->features->add(nn->Flatten());
$self->features->add(nn->Dense(4096, activation=>'relu'));
$self->features->add(nn->Dropout(0.5));
$self->features->add(nn->Dense(4096, activation=>'relu'));
$self->features->add(nn->Dropout(0.5));
});
$self->output(nn->Dense($self->classes));
});
}
method hybrid_forward(GluonClass $F, GluonInput $x)
{
$x = $self->features->($x);
$x = $self->output->($x);
return $x;
}
package AI::MXNet::Gluon::ModelZoo::Vision;
=head2 alexnet
AlexNet model from the `"One weird trick..." <https://arxiv.org/abs/1404.5997> paper.
lib/AI/MXNet/Gluon/ModelZoo/Vision/DenseNet.pm view on Meta::CPAN
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
package AI::MXNet::Gluon::ModelZoo::Vision::DenseNet;
use strict;
use warnings;
use AI::MXNet::Base;
use AI::MXNet::Function::Parameters;
use AI::MXNet::Gluon::Mouse;
extends 'AI::MXNet::Gluon::HybridBlock';
lib/AI/MXNet/Gluon/ModelZoo/Vision/DenseNet.pm view on Meta::CPAN
for(1..$num_layers)
{
$out->add(_make_dense_layer($growth_rate, $bn_size, $dropout));
}
});
return $out;
}
func _make_dense_layer($growth_rate, $bn_size, $dropout)
{
my $new_features = nn->HybridSequential(prefix=>'');
$new_features->add(nn->BatchNorm());
$new_features->add(nn->Activation('relu'));
$new_features->add(nn->Conv2D($bn_size * $growth_rate, kernel_size=>1, use_bias=>0));
$new_features->add(nn->BatchNorm());
$new_features->add(nn->Activation('relu'));
$new_features->add(nn->Conv2D($growth_rate, kernel_size=>3, padding=>1, use_bias=>0));
if($dropout)
{
$new_features->add(nn->Dropout($dropout));
}
my $out = nn->HybridConcurrent(axis=>1, prefix=>'');
$out->add(nn->Identity());
$out->add($new_features);
return $out;
}
func _make_transition($num_output_features)
{
my $out = nn->HybridSequential(prefix=>'');
$out->add(nn->BatchNorm());
$out->add(nn->Activation('relu'));
$out->add(nn->Conv2D($num_output_features, kernel_size=>1, use_bias=>0));
$out->add(nn->AvgPool2D(pool_size=>2, strides=>2));
return $out;
}
=head1 NAME
AI::MXNet::Gluon::ModelZoo::Vision::DenseNet - Densenet-BC model from the "Densely Connected Convolutional Networks"
=cut
=head1 DESCRIPTION
Densenet-BC model from the "Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf> paper.
Parameters
----------
num_init_features : Int
Number of filters to learn in the first convolution layer.
growth_rate : Int
Number of filters to add each layer (`k` in the paper).
block_config : array ref of Int
List of integers for numbers of layers in each pooling block.
bn_size : Int, default 4
Multiplicative factor for number of bottle neck layers.
(i.e. bn_size * k features in the bottleneck layer)
dropout : float, default 0
Rate of dropout after each dense layer.
classes : int, default 1000
Number of classification classes.
=cut
has [qw/num_init_features
growth_rate/] => (is => 'ro', isa => 'Int', required => 1);
has 'block_config' => (is => 'ro', isa => 'ArrayRef[Int]', required => 1);
has 'bn_size' => (is => 'ro', isa => 'Int', default => 4);
has 'dropout' => (is => 'ro', isa => 'Num', default => 0);
has 'classes' => (is => 'ro', isa => 'Int', default => 1000);
method python_constructor_arguments(){ [qw/num_init_features growth_rate block_config bn_size dropout classes/] }
sub BUILD
{
my $self = shift;
$self->name_scope(sub {
$self->features(nn->HybridSequential(prefix=>''));
$self->features->add(
nn->Conv2D(
$self->num_init_features, kernel_size=>7,
strides=>2, padding=>3, use_bias=>0
)
);
$self->features->add(nn->BatchNorm());
$self->features->add(nn->Activation('relu'));
$self->features->add(nn->MaxPool2D(pool_size=>3, strides=>2, padding=>1));
# Add dense blocks
my $num_features = $self->num_init_features;
for(enumerate($self->block_config))
{
my ($i, $num_layers) = @$_;
$self->features->add(_make_dense_block($num_layers, $self->bn_size, $self->growth_rate, $self->dropout, $i+1));
$num_features += $num_layers * $self->growth_rate;
if($i != @{ $self->block_config } - 1)
{
$self->features->add(_make_transition(int($num_features/2)));
$num_features = int($num_features/2);
}
}
$self->features->add(nn->BatchNorm());
$self->features->add(nn->Activation('relu'));
$self->features->add(nn->AvgPool2D(pool_size=>7));
$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;
my %densenet_spec = (
121 => [64, 32, [6, 12, 24, 16]],
161 => [96, 48, [6, 12, 36, 24]],
169 => [64, 32, [6, 12, 32, 32]],
lib/AI/MXNet/Gluon/ModelZoo/Vision/DenseNet.pm view on Meta::CPAN
=cut
method get_densenet(
Int $num_layers, Bool :$pretrained=0, :$ctx=AI::MXNet::Context->cpu(),
:$root='~/.mxnet/models',
Int :$bn_size=4,
Num :$dropout=0,
Int :$classes=1000
)
{
my ($num_init_features, $growth_rate, $block_config) = @{ $densenet_spec{$num_layers} };
my $net = AI::MXNet::Gluon::ModelZoo::Vision::DenseNet->new(
$num_init_features, $growth_rate, $block_config,
$bn_size, $dropout, $classes
);
if($pretrained)
{
$net->load_parameters(
AI::MXNet::Gluon::ModelZoo::ModelStore->get_model_file(
"densenet$num_layers",
root=>$root
),
ctx=>$ctx
lib/AI/MXNet/Gluon/ModelZoo/Vision/Inception.pm view on Meta::CPAN
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "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.
package AI::MXNet::Gluon::ModelZoo::Vision::Inception::V3;
use strict;
use warnings;
use AI::MXNet::Base;
use AI::MXNet::Function::Parameters;
use AI::MXNet::Gluon::Mouse;
extends 'AI::MXNet::Gluon::HybridBlock';
lib/AI/MXNet/Gluon/ModelZoo/Vision/Inception.pm view on Meta::CPAN
if(defined $value)
{
$kwargs{ $setting_names[$i] } = $value;
}
}
$out->add(_make_basic_conv(%kwargs));
}
return $out;
}
func _make_A($pool_features, $prefix)
{
my $out = nn->HybridConcurrent(axis=>1, prefix=>$prefix);
$out->name_scope(sub {
$out->add(_make_branch('', [64, 1, undef, undef]));
$out->add(_make_branch(
'',
[48, 1, undef, undef],
[64, 5, undef, 2]
));
$out->add(_make_branch(
'',
[64, 1, undef, undef],
[96, 3, undef, 1],
[96, 3, undef, 1]
));
$out->add(_make_branch('avg', [$pool_features, 1, undef, undef]));
});
return $out;
}
func _make_B($prefix)
{
my $out = nn->HybridConcurrent(axis=>1, prefix=>$prefix);
$out->name_scope(sub {
$out->add(_make_branch('', [384, 3, 2, undef]));
$out->add(_make_branch(
lib/AI/MXNet/Gluon/ModelZoo/Vision/Inception.pm view on Meta::CPAN
Number of classification classes.
=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->add(_make_basic_conv(channels=>32, kernel_size=>3, strides=>2));
$self->features->add(_make_basic_conv(channels=>32, kernel_size=>3));
$self->features->add(_make_basic_conv(channels=>64, kernel_size=>3, padding=>1));
$self->features->add(nn->MaxPool2D(pool_size=>3, strides=>2));
$self->features->add(_make_basic_conv(channels=>80, kernel_size=>1));
$self->features->add(_make_basic_conv(channels=>192, kernel_size=>3));
$self->features->add(nn->MaxPool2D(pool_size=>3, strides=>2));
$self->features->add(_make_A(32, 'A1_'));
$self->features->add(_make_A(64, 'A2_'));
$self->features->add(_make_A(64, 'A3_'));
$self->features->add(_make_B('B_'));
$self->features->add(_make_C(128, 'C1_'));
$self->features->add(_make_C(160, 'C2_'));
$self->features->add(_make_C(160, 'C3_'));
$self->features->add(_make_C(192, 'C4_'));
$self->features->add(_make_D('D_'));
$self->features->add(_make_E('E1_'));
$self->features->add(_make_E('E2_'));
$self->features->add(nn->AvgPool2D(pool_size=>8));
$self->features->add(nn->Dropout(0.5));
$self->output(nn->Dense($self->classes));
});
}
method hybrid_forward(GluonClass $F, GluonInput $x)
{
$x = $self->features->($x);
$x = $self->output->($x);
return $x;
}
package AI::MXNet::Gluon::ModelZoo::Vision;
=head2 inception_v3
Inception v3 model from
"Rethinking the Inception Architecture for Computer Vision"
lib/AI/MXNet/Gluon/ModelZoo/Vision/MobileNet.pm view on Meta::CPAN
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
use strict;
use warnings;
use AI::MXNet::Function::Parameters;
package AI::MXNet::Gluon::ModelZoo::Vision::MobileNet::RELU6;
use AI::MXNet::Gluon::Mouse;
extends 'AI::MXNet::Gluon::HybridBlock';
lib/AI/MXNet/Gluon/ModelZoo/Vision/MobileNet.pm view on Meta::CPAN
{
_add_conv($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 $x;
}
package AI::MXNet::Gluon::ModelZoo::Vision::MobileNetV2;
use AI::MXNet::Gluon::Mouse;
use AI::MXNet::Base;
extends 'AI::MXNet::Gluon::HybridBlock';
has 'multiplier' => (is => 'ro', isa => 'Num', default => 1);
has 'classes' => (is => 'ro', isa => 'Int', default => 1000);
lib/AI/MXNet/Gluon/ModelZoo/Vision/MobileNet.pm view on Meta::CPAN
if($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"
lib/AI/MXNet/Gluon/ModelZoo/Vision/ResNet.pm view on Meta::CPAN
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "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::ResNet::BasicBlockV1;
use AI::MXNet::Gluon::Mouse;
extends 'AI::MXNet::Gluon::HybridBlock';
lib/AI/MXNet/Gluon/ModelZoo/Vision/ResNet.pm view on Meta::CPAN
$self->downsample->add(nn->BatchNorm());
}
else
{
$self->downsample(undef);
}
}
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
$self->downsample->add(nn->BatchNorm());
}
else
{
$self->downsample(undef);
}
}
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';
=head1 NAME
AI::MXNet::Gluon::ModelZoo::Vision::ResNet::BottleneckV2 - Bottleneck 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);
$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';
use AI::MXNet::Base;
=head1 NAME
lib/AI/MXNet/Gluon/ModelZoo/Vision/ResNet.pm view on Meta::CPAN
=cut
=head1 DESCRIPTION
ResNet V1 model from from "Deep Residual Learning for Image Recognition"
<http://arxiv.org/abs/1512.03385> paper.
Parameters
----------
block : AI::MXNet::Gluon::HybridBlock
Class for the residual block. Options are AI::MXNet::Gluon::ModelZoo::Vision::ResNet::BasicBlockV1,
AI::MXNet::Gluon::ModelZoo::Vision::ResNet::BottleneckV1.
layers : array ref of Int
Numbers of layers in each block
channels : array ref of Int
Numbers of channels in each block. Length should be one larger than layers list.
classes : int, default 1000
Number of classification classes.
thumbnail : bool, default 0
Enable thumbnail.
=cut
lib/AI/MXNet/Gluon/ModelZoo/Vision/ResNet.pm view on Meta::CPAN
$channels, kernel_size=>3, strides=>$stride, padding=>1,
use_bias=>0, in_channels=>$in_channels
);
}
sub BUILD
{
my $self = shift;
assert(@{ $self->layers } == (@{ $self->channels } - 1));
$self->name_scope(sub {
$self->features(nn->HybridSequential(prefix=>''));
if($self->thumbnail)
{
$self->features->add(_conv3x3($self->channels->[0], 1, 0));
}
else
{
$self->features->add(nn->Conv2D($self->channels->[0], 7, 2, 3, use_bias=>0));
$self->features->add(nn->BatchNorm());
$self->features->add(nn->Activation('relu'));
$self->features->add(nn->MaxPool2D(3, 2, 1));
}
for(enumerate($self->layers))
{
my ($i, $num_layer) = @$_;
my $stride = $i == 0 ? 1 : 2;
$self->features->add(
$self->_make_layer(
$self->block, $num_layer, $self->channels->[$i+1],
$stride, $i+1, in_channels=>$self->channels->[$i]
)
);
}
$self->features->add(nn->GlobalAvgPool2D());
$self->output(nn->Dense($self->classes, in_units=>$self->channels->[-1]));
});
}
method _make_layer($block, $layers, $channels, $stride, $stage_index, :$in_channels=0)
{
my $layer = nn->HybridSequential(prefix=>"stage${stage_index}_");
$layer->name_scope(sub {
$layer->add(
$block->new(
lib/AI/MXNet/Gluon/ModelZoo/Vision/ResNet.pm view on Meta::CPAN
for(1..$layers-1)
{
$layer->add($block->new($channels, 1, 0, in_channels=>$channels, prefix=>''));
}
});
return $layer;
}
method hybrid_forward(GluonClass $F, GluonInput $x)
{
$x = $self->features->($x);
$x = $self->output->($x);
return $x;
}
package AI::MXNet::Gluon::ModelZoo::Vision::ResNet::V2;
use AI::MXNet::Gluon::Mouse;
extends 'AI::MXNet::Gluon::HybridBlock';
use AI::MXNet::Base;
lib/AI/MXNet/Gluon/ModelZoo/Vision/ResNet.pm view on Meta::CPAN
=cut
=head1 DESCRIPTION
ResNet V2 model from "Identity Mappings in Deep Residual Networks"
<https://arxiv.org/abs/1603.05027> paper.
Parameters
----------
block : AI::MXNet::Gluon::HybridBlock
Class for the residual block. Options are AI::MXNet::Gluon::ModelZoo::Vision::ResNet::BasicBlockV2,
AI::MXNet::Gluon::ModelZoo::Vision::ResNet::BottleneckV2.
layers : array ref of Int
Numbers of layers in each block
channels : array ref of Int
Numbers of channels in each block. Length should be one larger than layers list.
classes : int, default 1000
Number of classification classes.
thumbnail : bool, default 0
Enable thumbnail.
=cut
lib/AI/MXNet/Gluon/ModelZoo/Vision/ResNet.pm view on Meta::CPAN
$channels, kernel_size=>3, strides=>$stride, padding=>1,
use_bias=>0, in_channels=>$in_channels
);
}
sub BUILD
{
my $self = shift;
assert(@{ $self->layers } == (@{ $self->channels } - 1));
$self->name_scope(sub {
$self->features(nn->HybridSequential(prefix=>''));
$self->features->add(nn->BatchNorm(scale=>0, center=>0));
if($self->thumbnail)
{
$self->features->add(_conv3x3($self->channels->[0], 1, 0));
}
else
{
$self->features->add(nn->Conv2D($self->channels->[0], 7, 2, 3, use_bias=>0));
$self->features->add(nn->BatchNorm());
$self->features->add(nn->Activation('relu'));
$self->features->add(nn->MaxPool2D(3, 2, 1));
}
my $in_channels = $self->channels->[0];
for(enumerate($self->layers))
{
my ($i, $num_layer) = @$_;
my $stride = $i == 0 ? 1 : 2;
$self->features->add(
$self->_make_layer(
$self->block, $num_layer, $self->channels->[$i+1],
$stride, $i+1, in_channels=>$in_channels
)
);
$in_channels = $self->channels->[$i+1];
}
$self->features->add(nn->BatchNorm());
$self->features->add(nn->Activation('relu'));
$self->features->add(nn->GlobalAvgPool2D());
$self->features->add(nn->Flatten());
$self->output(nn->Dense($self->classes, in_units=>$in_channels));
});
}
method _make_layer($block, $layers, $channels, $stride, $stage_index, :$in_channels=0)
{
my $layer = nn->HybridSequential(prefix=>"stage${stage_index}_");
$layer->name_scope(sub {
$layer->add(
$block->new(
lib/AI/MXNet/Gluon/ModelZoo/Vision/ResNet.pm view on Meta::CPAN
for(1..$layers-1)
{
$layer->add($block->new($channels, 1, 0, in_channels=>$channels, prefix=>''));
}
});
return $layer;
}
method hybrid_forward(GluonClass $F, GluonInput $x)
{
$x = $self->features->($x);
$x = $self->output->($x);
return $x;
}
package AI::MXNet::Gluon::ModelZoo::Vision;
# Specification
my %resnet_spec = (
18 => ['basic_block', [2, 2, 2, 2], [64, 64, 128, 256, 512]],
34 => ['basic_block', [3, 4, 6, 3], [64, 64, 128, 256, 512]],
50 => ['bottle_neck', [3, 4, 6, 3], [64, 256, 512, 1024, 2048]],
101 => ['bottle_neck', [3, 4, 23, 3], [64, 256, 512, 1024, 2048]],
152 => ['bottle_neck', [3, 8, 36, 3], [64, 256, 512, 1024, 2048]]
);
my @resnet_net_versions = qw(AI::MXNet::Gluon::ModelZoo::Vision::ResNet::V1 AI::MXNet::Gluon::ModelZoo::Vision::ResNet::V2);
my @resnet_block_versions = (
{
basic_block => 'AI::MXNet::Gluon::ModelZoo::Vision::ResNet::BasicBlockV1',
bottle_neck => 'AI::MXNet::Gluon::ModelZoo::Vision::ResNet::BottleneckV1'
},
{
basic_block => 'AI::MXNet::Gluon::ModelZoo::Vision::ResNet::BasicBlockV2',
bottle_neck => 'AI::MXNet::Gluon::ModelZoo::Vision::ResNet::BottleneckV2'
},
);
=head2 get_resnet
ResNet V1 model from "Deep Residual Learning for Image Recognition"
<http://arxiv.org/abs/1512.03385> paper.
ResNet V2 model from "Identity Mappings in Deep Residual Networks"
<https://arxiv.org/abs/1603.05027> paper.
Parameters
----------
$version : Int
Version of ResNet. Options are 1, 2.
lib/AI/MXNet/Gluon/ModelZoo/Vision/ResNet.pm view on Meta::CPAN
Numbers of layers. Options are 18, 34, 50, 101, 152.
:$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
# 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$version",
root=>$root
),
ctx=>$ctx
);
}
return $net;
}
=head2 resnet18_v1
ResNet-18 V1 model from "Deep Residual Learning for Image Recognition"
<http://arxiv.org/abs/1512.03385> paper.
Parameters
----------
:$pretrained : Bool, default 0
Whether to load the pretrained weights for model.
:$ctx : AI::MXNet::Context, default CPU
The context in which to load the pretrained weights.
:$root : Str, default '~/.mxnet/models'
Location for keeping the model parameters.
=cut
method resnet18_v1(%kwargs)
{
return __PACKAGE__->get_resnet(1, 18, %kwargs);
}
=head2 resnet34_v1
ResNet-34 V1 model from "Deep Residual Learning for Image Recognition"
<http://arxiv.org/abs/1512.03385> 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.
:$root : Str, default '~/.mxnet/models'
Location for keeping the model parameters.
=cut
method resnet34_v1(%kwargs)
{
return __PACKAGE__->get_resnet(1, 34, %kwargs);
}
=head2 resnet50_v1
ResNet-50 V1 model from "Deep Residual Learning for Image Recognition"
<http://arxiv.org/abs/1512.03385> paper.
Parameters
----------
:$pretrained : Bool, default 0
Whether to load the pretrained weights for model.
:$ctx : AI::MXNet::Context, default CPU
The context in which to load the pretrained weights.
:$root : Str, default '~/.mxnet/models'
Location for keeping the model parameters.
=cut
method resnet50_v1(%kwargs)
{
return __PACKAGE__->get_resnet(1, 50, %kwargs);
}
=head2 resnet101_v1
ResNet-101 V1 model from "Deep Residual Learning for Image Recognition"
<http://arxiv.org/abs/1512.03385> 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.
:$root : Str, default '~/.mxnet/models'
Location for keeping the model parameters.
=cut
method resnet101_v1(%kwargs)
{
return __PACKAGE__->get_resnet(1, 101, %kwargs);
}
=head2 resnet152_v1
ResNet-152 V1 model from "Deep Residual Learning for Image Recognition"
<http://arxiv.org/abs/1512.03385> paper.
Parameters
----------
:$pretrained : Bool, default 0
Whether to load the pretrained weights for model.
:$ctx : AI::MXNet::Context, default CPU
The context in which to load the pretrained weights.
:$root : Str, default '~/.mxnet/models'
Location for keeping the model parameters.
=cut
method resnet152_v1(%kwargs)
{
return __PACKAGE__->get_resnet(1, 152, %kwargs);
}
=head2 resnet18_v2
ResNet-18 V2 model from "Identity Mappings in Deep Residual Networks"
<https://arxiv.org/abs/1603.05027> 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.
:$root : Str, default '~/.mxnet/models'
Location for keeping the model parameters.
=cut
method resnet18_v2(%kwargs)
{
return __PACKAGE__->get_resnet(2, 18, %kwargs);
}
=head2 resnet34_v2
ResNet-34 V2 model from "Identity Mappings in Deep Residual Networks"
<https://arxiv.org/abs/1603.05027> paper.
Parameters
----------
:$pretrained : Bool, default 0
Whether to load the pretrained weights for model.
:$ctx : AI::MXNet::Context, default CPU
The context in which to load the pretrained weights.
:$root : Str, default '~/.mxnet/models'
Location for keeping the model parameters.
=cut
method resnet34_v2(%kwargs)
{
return __PACKAGE__->get_resnet(2, 34, %kwargs);
}
=head2 resnet50_v2
ResNet-50 V2 model from "Identity Mappings in Deep Residual Networks"
<https://arxiv.org/abs/1603.05027> 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.
:$root : Str, default '~/.mxnet/models'
Location for keeping the model parameters.
=cut
method resnet50_v2(%kwargs)
{
return __PACKAGE__->get_resnet(2, 50, %kwargs);
}
=head2 resnet101_v2
ResNet-101 V2 model from "Identity Mappings in Deep Residual Networks"
<https://arxiv.org/abs/1603.05027> paper.
Parameters
----------
:$pretrained : Bool, default 0
Whether to load the pretrained weights for model.
:$ctx : AI::MXNet::Context, default CPU
The context in which to load the pretrained weights.
:$root : Str, default '~/.mxnet/models'
Location for keeping the model parameters.
=cut
method resnet101_v2(%kwargs)
{
return __PACKAGE__->get_resnet(2, 101, %kwargs);
}
=head2 resnet152_v2
ResNet-152 V2 model from "Identity Mappings in Deep Residual Networks"
<https://arxiv.org/abs/1603.05027> 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.
:$root : Str, default '~/.mxnet/models'
Location for keeping the model parameters.
=cut
method resnet152_v2(%kwargs)
{
return __PACKAGE__->get_resnet(2, 152, %kwargs);
}
1;
lib/AI/MXNet/Gluon/ModelZoo/Vision/SqueezeNet.pm view on Meta::CPAN
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "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.
package AI::MXNet::Gluon::ModelZoo::Vision::SqueezeNet;
use strict;
use warnings;
use AI::MXNet::Function::Parameters;
use AI::MXNet::Gluon::Mouse;
use AI::MXNet::Types;
extends 'AI::MXNet::Gluon::HybridBlock';
lib/AI/MXNet/Gluon/ModelZoo/Vision/SqueezeNet.pm view on Meta::CPAN
=cut
has 'version' => (is => 'ro', isa => enum([qw[1.0 1.1]]), required => 1);
has 'classes' => (is => 'ro', isa => 'Int', default => 1000);
method python_constructor_arguments() { [qw/version classes/] }
sub BUILD
{
my $self = shift;
$self->name_scope(sub {
$self->features(nn->HybridSequential(prefix=>''));
if($self->version eq '1.0')
{
$self->features->add(nn->Conv2D(96, kernel_size=>7, strides=>2));
$self->features->add(nn->Activation('relu'));
$self->features->add(nn->MaxPool2D(pool_size=>3, strides=>2, ceil_mode=>1));
$self->features->add(_make_fire(16, 64, 64));
$self->features->add(_make_fire(16, 64, 64));
$self->features->add(_make_fire(32, 128, 128));
$self->features->add(nn->MaxPool2D(pool_size=>3, strides=>2, ceil_mode=>1));
$self->features->add(_make_fire(32, 128, 128));
$self->features->add(_make_fire(48, 192, 192));
$self->features->add(_make_fire(48, 192, 192));
$self->features->add(_make_fire(64, 256, 256));
$self->features->add(nn->MaxPool2D(pool_size=>3, strides=>2, ceil_mode=>1));
$self->features->add(_make_fire(64, 256, 256));
}
else
{
$self->features->add(nn->Conv2D(64, kernel_size=>3, strides=>2));
$self->features->add(nn->Activation('relu'));
$self->features->add(nn->MaxPool2D(pool_size=>3, strides=>2, ceil_mode=>1));
$self->features->add(_make_fire(16, 64, 64));
$self->features->add(_make_fire(16, 64, 64));
$self->features->add(nn->MaxPool2D(pool_size=>3, strides=>2, ceil_mode=>1));
$self->features->add(_make_fire(32, 128, 128));
$self->features->add(_make_fire(32, 128, 128));
$self->features->add(nn->MaxPool2D(pool_size=>3, strides=>2, ceil_mode=>1));
$self->features->add(_make_fire(48, 192, 192));
$self->features->add(_make_fire(48, 192, 192));
$self->features->add(_make_fire(64, 256, 256));
$self->features->add(_make_fire(64, 256, 256));
}
$self->features->add(nn->Dropout(0.5));
$self->output(nn->HybridSequential(prefix=>''));
$self->output->add(nn->Conv2D($self->classes, kernel_size=>1));
$self->output->add(nn->Activation('relu'));
$self->output->add(nn->AvgPool2D(13));
$self->output->add(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_squeezenet
SqueezeNet model from the "SqueezeNet: AlexNet-level accuracy with 50x fewer parameters
lib/AI/MXNet/Gluon/ModelZoo/Vision/VGG.pm view on Meta::CPAN
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "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::VGG;
use AI::MXNet::Gluon::Mouse;
extends 'AI::MXNet::Gluon::HybridBlock';
use AI::MXNet::Base;
lib/AI/MXNet/Gluon/ModelZoo/Vision/VGG.pm view on Meta::CPAN
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,
weight_initializer=>'normal',
bias_initializer=>'zeros'));
});
}
method _make_features()
{
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,
lib/AI/MXNet/Gluon/ModelZoo/Vision/VGG.pm view on Meta::CPAN
}
$featurizer->add(nn->Activation('relu'));
}
$featurizer->add(nn->MaxPool2D(strides=>2));
}
return $featurizer;
}
method hybrid_forward(GluonClass $F, GluonInput $x)
{
$x = $self->features->($x);
$x = $self->output->($x);
return $x;
}
package AI::MXNet::Gluon::ModelZoo::Vision;
# Specification
my %vgg_spec = (
11 => [[1, 1, 2, 2, 2], [64, 128, 256, 512, 512]],
13 => [[2, 2, 2, 2, 2], [64, 128, 256, 512, 512]],
t/AI-MXNet-Gluon-ModelZoo.t view on Meta::CPAN
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "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 Test::More tests => 1;
BEGIN { use_ok('AI::MXNet::Gluon::ModelZoo') };
t/test_gluon_model_zoo.t view on Meta::CPAN
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "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');
my %pretrained_to_test = ('squeezenet1.1' => 1);
for my $model_name (@all_models)
( run in 0.782 second using v1.01-cache-2.11-cpan-49f99fa48dc )