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# coding: utf-8
# pylint: disable= arguments-differ,unused-argument,missing-docstring,too-many-function-args
"""MobileNet and MobileNetV2, implemented in Gluon."""

from mxnet.gluon import nn
from mxnet.gluon.nn import BatchNorm
from mxnet.context import cpu
from mxnet.gluon.block import HybridBlock
from ..nn import ReLU6


__all__ = [
    'MobileNet',
    'MobileNetV2',
    'mobilenet1_0',
    'mobilenet_v2_1_0',
    'mobilenet0_75',
    'mobilenet_v2_0_75',
    'mobilenet0_5',
    'mobilenet_v2_0_5',
    'mobilenet0_25',
    'mobilenet_v2_0_25',
    'get_mobilenet',
    'get_mobilenet_v2']

__modify__ = 'dwSun'
__modified_date__ = '18/04/18'

# pylint: disable= too-many-arguments
def _add_conv(out, channels=1, kernel=1, stride=1, pad=0,
              num_group=1, active=True, relu6=False, norm_layer=BatchNorm, norm_kwargs=None):
    out.add(nn.Conv2D(channels, kernel, stride, pad, groups=num_group, use_bias=False))
    out.add(norm_layer(scale=True, **({} if norm_kwargs is None else norm_kwargs)))
    if active:
        out.add(ReLU6() if relu6 else nn.Activation('relu'))


def _add_conv_dw(out, dw_channels, channels, stride, relu6=False,
                 norm_layer=BatchNorm, norm_kwargs=None):
    _add_conv(out, channels=dw_channels, kernel=3, stride=stride,
              pad=1, num_group=dw_channels, relu6=relu6,
              norm_layer=norm_layer, norm_kwargs=norm_kwargs)
    _add_conv(out, channels=channels, relu6=relu6,
              norm_layer=norm_layer, norm_kwargs=norm_kwargs)


class LinearBottleneck(nn.HybridBlock):
    r"""LinearBottleneck used in MobileNetV2 model from the
    `"Inverted Residuals and Linear Bottlenecks:
      Mobile Networks for Classification, Detection and Segmentation"
    <https://arxiv.org/abs/1801.04381>`_ paper.

    Parameters
    ----------
    in_channels : int
        Number of input channels.
    channels : int
        Number of output channels.
    t : int
        Layer expansion ratio.
    stride : int
        stride
    norm_layer : object
        Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
        Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
    norm_kwargs : dict
        Additional `norm_layer` arguments, for example `num_devices=4`
        for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
    """

    def __init__(self, in_channels, channels, t, stride,
                 norm_layer=BatchNorm, norm_kwargs=None, **kwargs):
        super(LinearBottleneck, self).__init__(**kwargs)
        self.use_shortcut = stride == 1 and in_channels == channels
        with self.name_scope():
            self.out = nn.HybridSequential()

            if t != 1:
                _add_conv(self.out,
                          in_channels * t,
                          relu6=True,
                          norm_layer=norm_layer, norm_kwargs=norm_kwargs)
            _add_conv(self.out,
                      in_channels * t,
                      kernel=3,
                      stride=stride,
                      pad=1,
                      num_group=in_channels * t,
                      relu6=True,
                      norm_layer=norm_layer, norm_kwargs=norm_kwargs)
            _add_conv(self.out,
                      channels,
                      active=False,
                      relu6=True,
                      norm_layer=norm_layer, norm_kwargs=norm_kwargs)

    def hybrid_forward(self, F, x):
        out = self.out(x)
        if self.use_shortcut:
            out = F.elemwise_add(out, x)
        return out


# Net
class MobileNet(HybridBlock):
    r"""MobileNet model from the
    `"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"
    <https://arxiv.org/abs/1704.04861>`_ paper.

    Parameters
    ----------
    multiplier : float, default 1.0
        The width multiplier for controlling the model size. Only multipliers that are no
        less than 0.25 are supported. The actual number of channels is equal to the original
        channel size multiplied by this multiplier.
    classes : int, default 1000
        Number of classes for the output layer.
    norm_layer : object
        Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
        Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
    norm_kwargs : dict
        Additional `norm_layer` arguments, for example `num_devices=4`
        for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
    """

    def __init__(self, multiplier=1.0, classes=1000,
                 norm_layer=BatchNorm, norm_kwargs=None, **kwargs):
        super(MobileNet, self).__init__(**kwargs)
        with self.name_scope():
            self.features = nn.HybridSequential(prefix='')
            with self.features.name_scope():
                _add_conv(self.features, channels=int(32 * multiplier), kernel=3, pad=1, stride=2,
                          norm_layer=norm_layer, norm_kwargs=norm_kwargs)
                dw_channels = [int(x * multiplier) for x in [32, 64] + [128] * 2 +
                               [256] *
                               2 +
                               [512] *
                               6 +
                               [1024]]
                channels = [int(x * multiplier) for x in [64] +
                            [128] * 2 + [256] * 2 + [512] * 6 + [1024] * 2]
                strides = [1, 2] * 3 + [1] * 5 + [2, 1]
                for dwc, c, s in zip(dw_channels, channels, strides):
                    _add_conv_dw(self.features, dw_channels=dwc, channels=c, stride=s,
                                 norm_layer=norm_layer, norm_kwargs=norm_kwargs)
                self.features.add(nn.GlobalAvgPool2D())
                self.features.add(nn.Flatten())

            self.output = nn.Dense(classes)

    def hybrid_forward(self, F, x):
        x = self.features(x)
        x = self.output(x)
        return x


class MobileNetV2(nn.HybridBlock):
    r"""MobileNetV2 model from the
    `"Inverted Residuals and Linear Bottlenecks:
      Mobile Networks for Classification, Detection and Segmentation"
    <https://arxiv.org/abs/1801.04381>`_ paper.
    Parameters
    ----------
    multiplier : float, default 1.0
        The width multiplier for controlling the model size. The actual number of channels
        is equal to the original channel size multiplied by this multiplier.
    classes : int, default 1000
        Number of classes for the output layer.
    norm_layer : object
        Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
        Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
    norm_kwargs : dict
        Additional `norm_layer` arguments, for example `num_devices=4`
        for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
    """

    def __init__(self, multiplier=1.0, classes=1000,
                 norm_layer=BatchNorm, norm_kwargs=None, **kwargs):
        super(MobileNetV2, self).__init__(**kwargs)
        with self.name_scope():
            self.features = nn.HybridSequential(prefix='features_')
            with self.features.name_scope():
                _add_conv(self.features, int(32 * multiplier), kernel=3,
                          stride=2, pad=1, relu6=True,
                          norm_layer=norm_layer, norm_kwargs=norm_kwargs)

                in_channels_group = [int(x * multiplier) for x in [32] + [16] + [24] * 2
                                     + [32] * 3 + [64] * 4 + [96] * 3 + [160] * 3]
                channels_group = [int(x * multiplier) for x in [16] + [24] * 2 + [32] * 3
                                  + [64] * 4 + [96] * 3 + [160] * 3 + [320]]
                ts = [1] + [6] * 16
                strides = [1, 2] * 2 + [1, 1, 2] + [1] * 6 + [2] + [1] * 3

                for in_c, c, t, s in zip(in_channels_group, channels_group, ts, strides):
                    self.features.add(LinearBottleneck(in_channels=in_c,
                                                       channels=c,
                                                       t=t,
                                                       stride=s,
                                                       norm_layer=norm_layer,
                                                       norm_kwargs=norm_kwargs))

                last_channels = int(1280 * multiplier) if multiplier > 1.0 else 1280
                _add_conv(self.features,
                          last_channels,
                          relu6=True,
                          norm_layer=norm_layer, norm_kwargs=norm_kwargs)

                self.features.add(nn.GlobalAvgPool2D())

            self.output = nn.HybridSequential(prefix='output_')
            with self.output.name_scope():
                self.output.add(
                    nn.Conv2D(classes, 1, use_bias=False, prefix='pred_'),
                    nn.Flatten())

    def hybrid_forward(self, F, x):
        x = self.features(x)
        x = self.output(x)
        return x


# Constructor
def get_mobilenet(multiplier, pretrained=False, ctx=cpu(),
                  root='~/.mxnet/models', norm_layer=BatchNorm, norm_kwargs=None, **kwargs):
    r"""MobileNet model from the
    `"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"
    <https://arxiv.org/abs/1704.04861>`_ paper.

    Parameters
    ----------
    multiplier : float
        The width multiplier for controlling the model size. Only multipliers that are no
        less than 0.25 are supported. The actual number of channels is equal to the original
        channel size multiplied by this multiplier.
    pretrained : bool or str
        Boolean value controls whether to load the default pretrained weights for model.
        String value represents the hashtag for a certain version of pretrained weights.
    ctx : Context, default CPU
        The context in which to load the pretrained weights.
    root : str, default $MXNET_HOME/models
        Location for keeping the model parameters.
    norm_layer : object
        Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
        Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
    norm_kwargs : dict
        Additional `norm_layer` arguments, for example `num_devices=4`
        for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
    """
    net = MobileNet(multiplier, norm_layer=norm_layer, norm_kwargs=norm_kwargs, **kwargs)
    if pretrained:
        from .model_store import get_model_file
        version_suffix = '{0:.2f}'.format(multiplier)
        if version_suffix in ('1.00', '0.50'):
            version_suffix = version_suffix[:-1]
        net.load_parameters(get_model_file('mobilenet%s' % version_suffix,
                                           tag=pretrained,
                                           root=root), ctx=ctx)
        from ..data import ImageNet1kAttr
        attrib = ImageNet1kAttr()
        net.synset = attrib.synset
        net.classes = attrib.classes
        net.classes_long = attrib.classes_long
    return net


def get_mobilenet_v2(multiplier, pretrained=False, ctx=cpu(),
                     root='~/.mxnet/models', norm_layer=BatchNorm, norm_kwargs=None, **kwargs):
    r"""MobileNetV2 model from the
    `"Inverted Residuals and Linear Bottlenecks:
      Mobile Networks for Classification, Detection and Segmentation"
    <https://arxiv.org/abs/1801.04381>`_ paper.

    Parameters
    ----------
    multiplier : float
        The width multiplier for controlling the model size. Only multipliers that are no
        less than 0.25 are supported. The actual number of channels is equal to the original
        channel size multiplied by this multiplier.
    pretrained : bool or str
        Boolean value controls whether to load the default pretrained weights for model.
        String value represents the hashtag for a certain version of pretrained weights.
    ctx : Context, default CPU
        The context in which to load the pretrained weights.
    root : str, default $MXNET_HOME/models
        Location for keeping the model parameters.
    norm_layer : object
        Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
        Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
    norm_kwargs : dict
        Additional `norm_layer` arguments, for example `num_devices=4`
        for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
    """
    net = MobileNetV2(multiplier, norm_layer=norm_layer, norm_kwargs=norm_kwargs, **kwargs)

    if pretrained:
        from .model_store import get_model_file
        version_suffix = '{0:.2f}'.format(multiplier)
        if version_suffix in ('1.00', '0.50'):
            version_suffix = version_suffix[:-1]
        net.load_parameters(get_model_file('mobilenetv2_%s' % version_suffix,
                                           tag=pretrained,
                                           root=root), ctx=ctx)
        from ..data import ImageNet1kAttr
        attrib = ImageNet1kAttr()
        net.synset = attrib.synset
        net.classes = attrib.classes
        net.classes_long = attrib.classes_long
    return net


def mobilenet1_0(**kwargs):
    r"""MobileNet model from the
    `"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"
    <https://arxiv.org/abs/1704.04861>`_ paper, with width multiplier 1.0.

    Parameters
    ----------
    pretrained : bool or str
        Boolean value controls whether to load the default pretrained weights for model.
        String value represents the hashtag for a certain version of pretrained weights.
    ctx : Context, default CPU
        The context in which to load the pretrained weights.
    norm_layer : object
        Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
        Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
    norm_kwargs : dict
        Additional `norm_layer` arguments, for example `num_devices=4`
        for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
    """
    return get_mobilenet(1.0, **kwargs)


def mobilenet_v2_1_0(**kwargs):
    r"""MobileNetV2 model from the
    `"Inverted Residuals and Linear Bottlenecks:
      Mobile Networks for Classification, Detection and Segmentation"
    <https://arxiv.org/abs/1801.04381>`_ paper.

    Parameters
    ----------
    pretrained : bool or str
        Boolean value controls whether to load the default pretrained weights for model.
        String value represents the hashtag for a certain version of pretrained weights.
    ctx : Context, default CPU
        The context in which to load the pretrained weights.
    norm_layer : object
        Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
        Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
    norm_kwargs : dict
        Additional `norm_layer` arguments, for example `num_devices=4`
        for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
    """
    return get_mobilenet_v2(1.0, **kwargs)


def mobilenet0_75(**kwargs):
    r"""MobileNet model from the
    `"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"
    <https://arxiv.org/abs/1704.04861>`_ paper, with width multiplier 0.75.

    Parameters
    ----------
    pretrained : bool or str
        Boolean value controls whether to load the default pretrained weights for model.
        String value represents the hashtag for a certain version of pretrained weights.
    ctx : Context, default CPU
        The context in which to load the pretrained weights.
    norm_layer : object
        Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
        Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
    norm_kwargs : dict
        Additional `norm_layer` arguments, for example `num_devices=4`
        for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
    """
    return get_mobilenet(0.75, **kwargs)


def mobilenet_v2_0_75(**kwargs):
    r"""MobileNetV2 model from the
    `"Inverted Residuals and Linear Bottlenecks:
      Mobile Networks for Classification, Detection and Segmentation"
    <https://arxiv.org/abs/1801.04381>`_ paper.

    Parameters
    ----------
    pretrained : bool or str
        Boolean value controls whether to load the default pretrained weights for model.
        String value represents the hashtag for a certain version of pretrained weights.
    ctx : Context, default CPU
        The context in which to load the pretrained weights.
    norm_layer : object
        Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
        Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
    norm_kwargs : dict
        Additional `norm_layer` arguments, for example `num_devices=4`
        for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
    """
    return get_mobilenet_v2(0.75, **kwargs)


def mobilenet0_5(**kwargs):
    r"""MobileNet model from the
    `"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"
    <https://arxiv.org/abs/1704.04861>`_ paper, with width multiplier 0.5.

    Parameters
    ----------
    pretrained : bool or str
        Boolean value controls whether to load the default pretrained weights for model.
        String value represents the hashtag for a certain version of pretrained weights.
    ctx : Context, default CPU
        The context in which to load the pretrained weights.
    norm_layer : object
        Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
        Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
    norm_kwargs : dict
        Additional `norm_layer` arguments, for example `num_devices=4`
        for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
    """
    return get_mobilenet(0.5, **kwargs)


def mobilenet_v2_0_5(**kwargs):
    r"""MobileNetV2 model from the
    `"Inverted Residuals and Linear Bottlenecks:
      Mobile Networks for Classification, Detection and Segmentation"
    <https://arxiv.org/abs/1801.04381>`_ paper.

    Parameters
    ----------
    pretrained : bool or str
        Boolean value controls whether to load the default pretrained weights for model.
        String value represents the hashtag for a certain version of pretrained weights.
    ctx : Context, default CPU
        The context in which to load the pretrained weights.
    norm_layer : object
        Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
        Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
    norm_kwargs : dict
        Additional `norm_layer` arguments, for example `num_devices=4`
        for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
    """
    return get_mobilenet_v2(0.5, **kwargs)


def mobilenet0_25(**kwargs):
    r"""MobileNet model from the
    `"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"
    <https://arxiv.org/abs/1704.04861>`_ paper, with width multiplier 0.25.

    Parameters
    ----------
    pretrained : bool or str
        Boolean value controls whether to load the default pretrained weights for model.
        String value represents the hashtag for a certain version of pretrained weights.
    ctx : Context, default CPU
        The context in which to load the pretrained weights.
    norm_layer : object
        Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
        Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
    norm_kwargs : dict
        Additional `norm_layer` arguments, for example `num_devices=4`
        for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
    """
    return get_mobilenet(0.25, **kwargs)


def mobilenet_v2_0_25(**kwargs):
    r"""MobileNetV2 model from the
    `"Inverted Residuals and Linear Bottlenecks:
      Mobile Networks for Classification, Detection and Segmentation"
    <https://arxiv.org/abs/1801.04381>`_ paper.

    Parameters
    ----------
    pretrained : bool or str
        Boolean value controls whether to load the default pretrained weights for model.
        String value represents the hashtag for a certain version of pretrained weights.
    ctx : Context, default CPU
        The context in which to load the pretrained weights.
    norm_layer : object
        Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
        Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
    norm_kwargs : dict
        Additional `norm_layer` arguments, for example `num_devices=4`
        for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
    """
    return get_mobilenet_v2(0.25, **kwargs)
