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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';

=head1 NAME 

    AI::MXNet::Gluon::ModelZoo::Vision::ResNet::BasicBlockV1 - BasicBlock V1 from `"Deep Residual Learning for Image Recognition"
=cut

=head1 DESCRIPTION

    BasicBlock V1 from `"Deep Residual Learning for Image Recognition"
    <http://arxiv.org/abs/1512.03385>`_ paper.
    This is used for ResNet V1 for 18, 34 layers.

    Parameters
    ----------
    channels : Int
        Number of output channels.
    stride : Int
        Stride size.
    downsample : Bool, default 0
        Whether to downsample the input.
    in_channels : Int, default 0
        Number of input channels. Default is 0, to infer from the graph.
=cut

has ['channels',
      'stride']   => (is => 'ro', isa => 'Int', required => 1);
has 'downsample' => (is => 'rw', default => 0);
has 'in_channels' => (is => 'ro', isa => 'Int', default => 0);
method python_constructor_arguments() { [qw/channels stride downsample/] }
func _conv3x3($channels, $stride, $in_channels)
{
    return nn->Conv2D(
        $channels, kernel_size=>3, strides=>$stride, padding=>1,
        use_bias=>0, in_channels=>$in_channels
    );
}

sub BUILD
{
    my $self = shift;