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t/test_gluon_model_zoo.t  view on Meta::CPAN

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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)
    {
            my $test_pretrain = exists $pretrained_to_test{ $model_name };
            my $model = get_model($model_name, pretrained=>$test_pretrain, root=>'model/');
            my $data_shape = $model_name !~ /inception/ ? [2, 3, 224, 224] : [2, 3, 299, 299];
            if(not $test_pretrain)
            {
                $model->collect_params()->initialize();
            }
            $model->hybridize();
            $model->(mx->nd->random->uniform(shape=>$data_shape))->wait_to_read;
            ok(1, "forward for $model_name");
    }
}

test_models();