Source code for gluoncv.model_zoo.mobilenet
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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
[docs]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)
[docs]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())
# Constructor
[docs]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
[docs]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
[docs]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)
[docs]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)
[docs]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)
[docs]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)
[docs]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)
[docs]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)
[docs]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)
[docs]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)