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t/test_io.t  view on Meta::CPAN

use AI::MXNet qw(mx);
use Test::More tests => 31;
use AI::MXNet::TestUtils qw(same reldiff GetMNIST_ubyte GetCifar10);
use PDL;
use PDL::Types;
use PDL::NiceSlice;
$|++;


sub test_Cifar10Rec()
{
    GetCifar10();
    my $dataiter = mx->io->ImageRecordIter({
            path_imgrec => "data/cifar/train.rec",
            mean_img => "data/cifar/cifar10_mean.bin",
            rand_crop => 0,
            and_mirror => 0,
            shuffle => 0,
            data_shape => [3,28,28],
            batch_size => 100,

t/test_io.t  view on Meta::CPAN

        {
            $labelcount[int($nplabel->at($i)->asscalar)] += 1;
        }
    }
    for my $i (0..9)
    {
        ok($labelcount[$i] == 5000);
    }
}

sub test_NDArrayIter()
{
    my $datas  = ones(PDL::Type->new(6), 2, 2, 1000);
    my $labels = ones(PDL::Type->new(6), 1, 1000);
    for my $i (0..999)
    {
        $datas(:,:,$i) .= $i / 100;
        $labels(:,$i) .= $i / 100;
    }
    my $dataiter = mx->io->NDArrayIter(
        data => $datas,

t/test_io.t  view on Meta::CPAN

        {
            ok($labelcount[$i] == 124);
        }
        else
        {
            ok($labelcount[$i] == 100);
        }
    }
}

sub test_MNISTIter()
{
    GetMNIST_ubyte();

    my $batch_size = 100;
    my $train_dataiter = mx->io->MNISTIter({
            image => "data/train-images-idx3-ubyte",
            label => "data/train-labels-idx1-ubyte",
            data_shape => [784],
            batch_size => $batch_size,
            shuffle => 1,