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lib/AI/MXNet/Rtc.pm  view on Meta::CPAN

        list of input names and ndarray
    outputs : tuple of (str, mxnet.ndarray)
        list of output names and ndarray
    kernel : str
        the actual kernel code.
        Note that this is only the body of the kernel, i.e.
        after { and before }. Rtc will decorate the kernel.
        For example, if name = "mykernel" and
        inputs = [('x', mx.nd.zeros((10,)))]
        outputs = [('y', mx.nd.zeros((10,)))]
        kernel = "y[threadIdx.x] = x[threadIdx.x];",
        the kernel that is compile will be:
        extern "C" __global__ mykernel(float *x, float *y) {
            const int x_ndim = 1;
            const int x_dims = { 10 };
            const int y_ndim = 1;
            const int y_dims = { 10 };

            y[threadIdx.x] = x[threadIdx.x];
        }
=cut

has 'handle'              => (is => 'rw', isa => 'RtcHandle', init_arg => undef);
has [qw/name kernel/]     => (is => 'ro', isa => 'Str', required => 1);
has [qw/inputs outputs/]  => (is => 'ro', isa => 'HashRef[AI::MXNet::NDArray]', required => 1);

sub BUILD
{
    my $self = shift;

t/test_io.t  view on Meta::CPAN

{
    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,
            preprocess_threads => 4,
            prefetch_buffer => 1
    });
    my @labelcount;
    my $batchcount = 0;
    while(my $batch = <$dataiter>)
    {
        my $nplabel = $batch->label->[0];
        for my $i (0..$nplabel->shape->[0]-1)
        {
            $labelcount[int($nplabel->at($i)->asscalar)] += 1;