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# coding: utf-8
# pylint: disable= arguments-differ
"""MobileNet, implemented in Gluon."""
__all__ = ['MobileNet', 'mobilenet1_0', 'mobilenet0_75', 'mobilenet0_5', 'mobilenet0_25',
           'get_mobilenet']

import os

from ....context import cpu
from ...block import HybridBlock
from ... import nn

# Helpers
def _add_conv(out, channels=1, kernel=1, stride=1, pad=0, num_group=1):
    out.add(nn.Conv2D(channels, kernel, stride, pad, groups=num_group, use_bias=False))
    out.add(nn.BatchNorm(scale=False))
    out.add(nn.Activation('relu'))

def _add_conv_dw(out, dw_channels, channels, stride):
    _add_conv(out, channels=dw_channels, kernel=3, stride=stride, pad=1, num_group=dw_channels)
    _add_conv(out, channels=channels)


# Net
[docs]class MobileNet(HybridBlock):
    r"""MobileNet model from the
    `"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"
    `_ paper.

    Parameters
    ----------
    multiplier : float, default 1.0
        The width multiplier for controling 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.
    """
    def __init__(self, multiplier=1.0, classes=1000, **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)
                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)
                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

# Constructor
[docs]def get_mobilenet(multiplier, pretrained=False, ctx=cpu(),
                  root=os.path.join('~', '.mxnet', 'models'), **kwargs):
    r"""MobileNet model from the
    `"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"
    `_ paper.

    Parameters
    ----------
    multiplier : float
        The width multiplier for controling 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, default False
        Whether to load the pretrained weights for model.
    ctx : Context, default CPU
        The context in which to load the pretrained weights.
    root : str, default '~/.mxnet/models'
        Location for keeping the model parameters.
    """
    net = MobileNet(multiplier, **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_params(get_model_file('mobilenet%s'%version_suffix, root=root), ctx=ctx)
    return net

[docs]def mobilenet1_0(**kwargs):
    r"""MobileNet model from the
    `"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"
    `_ paper, with width multiplier 1.0.

    Parameters
    ----------
    pretrained : bool, default False
        Whether to load the pretrained weights for model.
    ctx : Context, default CPU
        The context in which to load the pretrained weights.
    """
    return get_mobilenet(1.0, **kwargs)

[docs]def mobilenet0_75(**kwargs):
    r"""MobileNet model from the
    `"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"
    `_ paper, with width multiplier 0.75.

    Parameters
    ----------
    pretrained : bool, default False
        Whether to load the pretrained weights for model.
    ctx : Context, default CPU
        The context in which to load the pretrained weights.
    """
    return get_mobilenet(0.75, **kwargs)

[docs]def mobilenet0_5(**kwargs):
    r"""MobileNet model from the
    `"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"
    `_ paper, with width multiplier 0.5.

    Parameters
    ----------
    pretrained : bool, default False
        Whether to load the pretrained weights for model.
    ctx : Context, default CPU
        The context in which to load the pretrained weights.
    """
    return get_mobilenet(0.5, **kwargs)

[docs]def mobilenet0_25(**kwargs):
    r"""MobileNet model from the
    `"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"
    `_ paper, with width multiplier 0.25.

    Parameters
    ----------
    pretrained : bool, default False
        Whether to load the pretrained weights for model.
    ctx : Context, default CPU
        The context in which to load the pretrained weights.
    """
    return get_mobilenet(0.25, **kwargs)