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# coding: utf-8
# pylint: disable= arguments-differ
"""MobileNet and MobileNetV2, implemented in Gluon."""
__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'
import os
from ... import nn
from ....context import cpu
from ...block import HybridBlock
from .... import base
# Helpers
class RELU6(nn.HybridBlock):
"""Relu6 used in MobileNetV2."""
def __init__(self, **kwargs):
super(RELU6, self).__init__(**kwargs)
def hybrid_forward(self, F, x):
return F.clip(x, 0, 6, name="relu6")
# pylint: disable= too-many-arguments
def _add_conv(out, channels=1, kernel=1, stride=1, pad=0,
num_group=1, active=True, relu6=False):
out.add(nn.Conv2D(channels, kernel, stride, pad, groups=num_group, use_bias=False))
out.add(nn.BatchNorm(scale=True))
if active:
out.add(RELU6() if relu6 else nn.Activation('relu'))
def _add_conv_dw(out, dw_channels, channels, stride, relu6=False):
_add_conv(out, channels=dw_channels, kernel=3, stride=stride,
pad=1, num_group=dw_channels, relu6=relu6)
_add_conv(out, channels=channels, relu6=relu6)
class LinearBottleneck(nn.HybridBlock):
r"""LinearBottleneck used in MobileNetV2 model from the
`"Inverted Residuals and Linear Bottlenecks:
Mobile Networks for Classification, Detection and Segmentation"
`_ paper.
Parameters
----------
in_channels : int
Number of input channels.
channels : int
Number of output channels.
t : int
Layer expansion ratio.
stride : int
stride
"""
def __init__(self, in_channels, channels, t, stride, **kwargs):
super(LinearBottleneck, self).__init__(**kwargs)
self.use_shortcut = stride == 1 and in_channels == channels
with self.name_scope():
self.out = nn.HybridSequential()
_add_conv(self.out, in_channels * t, relu6=True)
_add_conv(self.out, in_channels * t, kernel=3, stride=stride,
pad=1, num_group=in_channels * t, relu6=True)
_add_conv(self.out, channels, active=False, relu6=True)
def hybrid_forward(self, F, x):
out = self.out(x)
if self.use_shortcut:
out = F.elemwise_add(out, x)
return out
# 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
[docs]class MobileNetV2(nn.HybridBlock):
r"""MobileNetV2 model from the
`"Inverted Residuals and Linear Bottlenecks:
Mobile Networks for Classification, Detection and Segmentation"
`_ paper.
Parameters
----------
multiplier : float, default 1.0
The width multiplier for controling 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.
"""
def __init__(self, multiplier=1.0, classes=1000, **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)
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))
last_channels = int(1280 * multiplier) if multiplier > 1.0 else 1280
_add_conv(self.features, last_channels, relu6=True)
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
[docs]def get_mobilenet(multiplier, pretrained=False, ctx=cpu(),
root=os.path.join(base.data_dir(), '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_HOME/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_parameters(
get_model_file('mobilenet%s' % version_suffix, root=root), ctx=ctx)
return net
[docs]def get_mobilenet_v2(multiplier, pretrained=False, ctx=cpu(),
root=os.path.join(base.data_dir(), 'models'), **kwargs):
r"""MobileNetV2 model from the
`"Inverted Residuals and Linear Bottlenecks:
Mobile Networks for Classification, Detection and Segmentation"
`_ 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_HOME/models
Location for keeping the model parameters.
"""
net = MobileNetV2(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_parameters(
get_model_file('mobilenetv2_%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 mobilenet_v2_1_0(**kwargs):
r"""MobileNetV2 model from the
`"Inverted Residuals and Linear Bottlenecks:
Mobile Networks for Classification, Detection and Segmentation"
`_ paper.
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_v2(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 mobilenet_v2_0_75(**kwargs):
r"""MobileNetV2 model from the
`"Inverted Residuals and Linear Bottlenecks:
Mobile Networks for Classification, Detection and Segmentation"
`_ paper.
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_v2(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 mobilenet_v2_0_5(**kwargs):
r"""MobileNetV2 model from the
`"Inverted Residuals and Linear Bottlenecks:
Mobile Networks for Classification, Detection and Segmentation"
`_ paper.
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_v2(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)
[docs]def mobilenet_v2_0_25(**kwargs):
r"""MobileNetV2 model from the
`"Inverted Residuals and Linear Bottlenecks:
Mobile Networks for Classification, Detection and Segmentation"
`_ paper.
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_v2(0.25, **kwargs)
