AI-MXNet-Gluon-ModelZoo
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# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# 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';
func _make_basic_conv(%kwargs)
{
my $out = nn->HybridSequential(prefix=>'');
$out->add(nn->Conv2D(use_bias=>0, %kwargs));
$out->add(nn->BatchNorm(epsilon=>0.001));
$out->add(nn->Activation('relu'));
return $out;
}
func _make_branch($use_pool, @conv_settings)
{
my $out = nn->HybridSequential(prefix=>'');
if($use_pool eq 'avg')
{
$out->add(nn->AvgPool2D(pool_size=>3, strides=>1, padding=>1));
}
elsif($use_pool eq 'max')
{
$out->add(nn->MaxPool2D(pool_size=>3, strides=>2));
}
my @setting_names = ('channels', 'kernel_size', 'strides', 'padding');
for my $setting (@conv_settings)
{
my %kwargs;
for(enumerate($setting))
{
my ($i, $value) = @$_;
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(
'',
[64, 1, undef, undef],
[96, 3, undef, 1],
[96, 3, 2, undef]
));
$out->add(_make_branch('max'));
});
return $out;
}
func _make_C($channels_7x7, $prefix)
{
my $out = nn->HybridConcurrent(axis=>1, prefix=>$prefix);
$out->name_scope(sub {
$out->add(_make_branch('', [192, 1, undef, undef]));
$out->add(_make_branch(
'',
[$channels_7x7, 1, undef, undef],
[$channels_7x7, [1, 7], undef, [0, 3]],
[192, [7, 1], undef, [3, 0]]
));
$out->add(_make_branch(
'',
[$channels_7x7, 1, undef, undef],
[$channels_7x7, [7, 1], undef, [3, 0]],
[$channels_7x7, [1, 7], undef, [0, 3]],
[$channels_7x7, [7, 1], undef, [3, 0]],
[192, [1, 7], undef, [0, 3]]
));
$out->add(_make_branch(
'avg',
[192, 1, undef, undef]
));
});
return $out;
}
func _make_D($prefix)
{
my $out = nn->HybridConcurrent(axis=>1, prefix=>$prefix);
$out->name_scope(sub {
$out->add(_make_branch(
'',
[192, 1, undef, undef],
[320, 3, 2, undef]
));
$out->add(_make_branch(
'',
[192, 1, undef, undef],
[192, [1, 7], undef, [0, 3]],
[192, [7, 1], undef, [3, 0]],
[192, 3, 2, undef]
));
$out->add(_make_branch('max'));
});
return $out;
}
func _make_E($prefix)
{
my $out = nn->HybridConcurrent(axis=>1, prefix=>$prefix);
$out->name_scope(sub {
$out->add(_make_branch('', [320, 1, undef, undef]));
my $branch_3x3 = nn->HybridSequential(prefix=>'');
$out->add($branch_3x3);
$branch_3x3->add(_make_branch(
'',
[384, 1, undef, undef]
));
my $branch_3x3_split = nn->HybridConcurrent(axis=>1, prefix=>'');
$branch_3x3_split->add(_make_branch('', [384, [1, 3], undef, [0, 1]]));
$branch_3x3_split->add(_make_branch('', [384, [3, 1], undef, [1, 0]]));
$branch_3x3->add($branch_3x3_split);
my $branch_3x3dbl = nn->HybridSequential(prefix=>'');
$out->add($branch_3x3dbl);
$branch_3x3dbl->add(_make_branch(
'',
[448, 1, undef, undef],
[384, 3, undef, 1]
));
my $branch_3x3dbl_split = nn->HybridConcurrent(axis=>1, prefix=>'');
$branch_3x3dbl->add($branch_3x3dbl_split);
$branch_3x3dbl_split->add(_make_branch('', [384, [1, 3], undef, [0, 1]]));
$branch_3x3dbl_split->add(_make_branch('', [384, [3, 1], undef, [1, 0]]));
$out->add(_make_branch('avg', [192, 1, undef, undef]));
});
return $out;
}
func make_aux($classes)
{
my $out = nn->HybridSequential(prefix=>'');
$out->add(nn->AvgPool2D(pool_size=>5, strides=>3));
$out->add(_make_basic_conv(channels=>128, kernel_size=>1));
$out->add(_make_basic_conv(channels=>768, kernel_size=>5));
$out->add(nn->Flatten());
$out->add(nn->Dense($classes));
return $out;
}
=head1 NAME
AI::MXNet::Gluon::ModelZoo::Vision::Inception::V3 - Inception v3 model.
=cut
=head1 DESCRIPTION
Inception v3 model from
"Rethinking the Inception Architecture for Computer Vision"
<http://arxiv.org/abs/1512.00567> paper.
Parameters
----------
classes : Int, default 1000
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 $x;
}
package AI::MXNet::Gluon::ModelZoo::Vision;
=head2 inception_v3
Inception v3 model from
"Rethinking the Inception Architecture for Computer Vision"
<http://arxiv.org/abs/1512.00567> paper.
Parameters
----------
:$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 inception_v3(
Bool :$pretrained=0, AI::MXNet::Context :$ctx=AI::MXNet::Context->cpu(),
Str :$root='~/.mxnet/models', Int :$classes=1000
)
{
my $net = AI::MXNet::Gluon::ModelZoo::Vision::Inception::V3->new($classes);
if($pretrained)
{
$net->load_parameters(
AI::MXNet::Gluon::ModelZoo::ModelStore->get_model_file(
"inceptionv3",
root=>$root
),
ctx=>$ctx
);
}
return $net;
}
1;
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