Source code for mmseg.models.backbones.mobilenet_v3
# Copyright (c) OpenMMLab. All rights reserved.
import warnings
import mmcv
from mmcv.cnn import ConvModule
from mmcv.cnn.bricks import Conv2dAdaptivePadding
from mmcv.runner import BaseModule
from torch.nn.modules.batchnorm import _BatchNorm
from ..builder import BACKBONES
from ..utils import InvertedResidualV3 as InvertedResidual
[docs]@BACKBONES.register_module()
class MobileNetV3(BaseModule):
"""MobileNetV3 backbone.
This backbone is the improved implementation of `Searching for MobileNetV3
<https://ieeexplore.ieee.org/document/9008835>`_.
Args:
arch (str): Architecture of mobilnetv3, from {'small', 'large'}.
Default: 'small'.
conv_cfg (dict): Config dict for convolution layer.
Default: None, which means using conv2d.
norm_cfg (dict): Config dict for normalization layer.
Default: dict(type='BN').
out_indices (tuple[int]): Output from which layer.
Default: (0, 1, 12).
frozen_stages (int): Stages to be frozen (all param fixed).
Default: -1, which means not freezing any parameters.
norm_eval (bool): Whether to set norm layers to eval mode, namely,
freeze running stats (mean and var). Note: Effect on Batch Norm
and its variants only. Default: False.
with_cp (bool): Use checkpoint or not. Using checkpoint will save
some memory while slowing down the training speed.
Default: False.
pretrained (str, optional): model pretrained path. Default: None
init_cfg (dict or list[dict], optional): Initialization config dict.
Default: None
"""
# Parameters to build each block:
# [kernel size, mid channels, out channels, with_se, act type, stride]
arch_settings = {
'small': [[3, 16, 16, True, 'ReLU', 2], # block0 layer1 os=4
[3, 72, 24, False, 'ReLU', 2], # block1 layer2 os=8
[3, 88, 24, False, 'ReLU', 1],
[5, 96, 40, True, 'HSwish', 2], # block2 layer4 os=16
[5, 240, 40, True, 'HSwish', 1],
[5, 240, 40, True, 'HSwish', 1],
[5, 120, 48, True, 'HSwish', 1], # block3 layer7 os=16
[5, 144, 48, True, 'HSwish', 1],
[5, 288, 96, True, 'HSwish', 2], # block4 layer9 os=32
[5, 576, 96, True, 'HSwish', 1],
[5, 576, 96, True, 'HSwish', 1]],
'large': [[3, 16, 16, False, 'ReLU', 1], # block0 layer1 os=2
[3, 64, 24, False, 'ReLU', 2], # block1 layer2 os=4
[3, 72, 24, False, 'ReLU', 1],
[5, 72, 40, True, 'ReLU', 2], # block2 layer4 os=8
[5, 120, 40, True, 'ReLU', 1],
[5, 120, 40, True, 'ReLU', 1],
[3, 240, 80, False, 'HSwish', 2], # block3 layer7 os=16
[3, 200, 80, False, 'HSwish', 1],
[3, 184, 80, False, 'HSwish', 1],
[3, 184, 80, False, 'HSwish', 1],
[3, 480, 112, True, 'HSwish', 1], # block4 layer11 os=16
[3, 672, 112, True, 'HSwish', 1],
[5, 672, 160, True, 'HSwish', 2], # block5 layer13 os=32
[5, 960, 160, True, 'HSwish', 1],
[5, 960, 160, True, 'HSwish', 1]]
} # yapf: disable
def __init__(self,
arch='small',
conv_cfg=None,
norm_cfg=dict(type='BN'),
out_indices=(0, 1, 12),
frozen_stages=-1,
reduction_factor=1,
norm_eval=False,
with_cp=False,
pretrained=None,
init_cfg=None):
super(MobileNetV3, self).__init__(init_cfg)
self.pretrained = pretrained
assert not (init_cfg and pretrained), \
'init_cfg and pretrained cannot be setting at the same time'
if isinstance(pretrained, str):
warnings.warn('DeprecationWarning: pretrained is a deprecated, '
'please use "init_cfg" instead')
self.init_cfg = dict(type='Pretrained', checkpoint=pretrained)
elif pretrained is None:
if init_cfg is None:
self.init_cfg = [
dict(type='Kaiming', layer='Conv2d'),
dict(
type='Constant',
val=1,
layer=['_BatchNorm', 'GroupNorm'])
]
else:
raise TypeError('pretrained must be a str or None')
assert arch in self.arch_settings
assert isinstance(reduction_factor, int) and reduction_factor > 0
assert mmcv.is_tuple_of(out_indices, int)
for index in out_indices:
if index not in range(0, len(self.arch_settings[arch]) + 2):
raise ValueError(
'the item in out_indices must in '
f'range(0, {len(self.arch_settings[arch])+2}). '
f'But received {index}')
if frozen_stages not in range(-1, len(self.arch_settings[arch]) + 2):
raise ValueError('frozen_stages must be in range(-1, '
f'{len(self.arch_settings[arch])+2}). '
f'But received {frozen_stages}')
self.arch = arch
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.out_indices = out_indices
self.frozen_stages = frozen_stages
self.reduction_factor = reduction_factor
self.norm_eval = norm_eval
self.with_cp = with_cp
self.layers = self._make_layer()
def _make_layer(self):
layers = []
# build the first layer (layer0)
in_channels = 16
layer = ConvModule(
in_channels=3,
out_channels=in_channels,
kernel_size=3,
stride=2,
padding=1,
conv_cfg=dict(type='Conv2dAdaptivePadding'),
norm_cfg=self.norm_cfg,
act_cfg=dict(type='HSwish'))
self.add_module('layer0', layer)
layers.append('layer0')
layer_setting = self.arch_settings[self.arch]
for i, params in enumerate(layer_setting):
(kernel_size, mid_channels, out_channels, with_se, act,
stride) = params
if self.arch == 'large' and i >= 12 or self.arch == 'small' and \
i >= 8:
mid_channels = mid_channels // self.reduction_factor
out_channels = out_channels // self.reduction_factor
if with_se:
se_cfg = dict(
channels=mid_channels,
ratio=4,
act_cfg=(dict(type='ReLU'),
dict(type='HSigmoid', bias=3.0, divisor=6.0)))
else:
se_cfg = None
layer = InvertedResidual(
in_channels=in_channels,
out_channels=out_channels,
mid_channels=mid_channels,
kernel_size=kernel_size,
stride=stride,
se_cfg=se_cfg,
with_expand_conv=(in_channels != mid_channels),
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=dict(type=act),
with_cp=self.with_cp)
in_channels = out_channels
layer_name = 'layer{}'.format(i + 1)
self.add_module(layer_name, layer)
layers.append(layer_name)
# build the last layer
# block5 layer12 os=32 for small model
# block6 layer16 os=32 for large model
layer = ConvModule(
in_channels=in_channels,
out_channels=576 if self.arch == 'small' else 960,
kernel_size=1,
stride=1,
dilation=4,
padding=0,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=dict(type='HSwish'))
layer_name = 'layer{}'.format(len(layer_setting) + 1)
self.add_module(layer_name, layer)
layers.append(layer_name)
# next, convert backbone MobileNetV3 to a semantic segmentation version
if self.arch == 'small':
self.layer4.depthwise_conv.conv.stride = (1, 1)
self.layer9.depthwise_conv.conv.stride = (1, 1)
for i in range(4, len(layers)):
layer = getattr(self, layers[i])
if isinstance(layer, InvertedResidual):
modified_module = layer.depthwise_conv.conv
else:
modified_module = layer.conv
if i < 9:
modified_module.dilation = (2, 2)
pad = 2
else:
modified_module.dilation = (4, 4)
pad = 4
if not isinstance(modified_module, Conv2dAdaptivePadding):
# Adjust padding
pad *= (modified_module.kernel_size[0] - 1) // 2
modified_module.padding = (pad, pad)
else:
self.layer7.depthwise_conv.conv.stride = (1, 1)
self.layer13.depthwise_conv.conv.stride = (1, 1)
for i in range(7, len(layers)):
layer = getattr(self, layers[i])
if isinstance(layer, InvertedResidual):
modified_module = layer.depthwise_conv.conv
else:
modified_module = layer.conv
if i < 13:
modified_module.dilation = (2, 2)
pad = 2
else:
modified_module.dilation = (4, 4)
pad = 4
if not isinstance(modified_module, Conv2dAdaptivePadding):
# Adjust padding
pad *= (modified_module.kernel_size[0] - 1) // 2
modified_module.padding = (pad, pad)
return layers
[docs] def forward(self, x):
outs = []
for i, layer_name in enumerate(self.layers):
layer = getattr(self, layer_name)
x = layer(x)
if i in self.out_indices:
outs.append(x)
return outs
def _freeze_stages(self):
for i in range(self.frozen_stages + 1):
layer = getattr(self, f'layer{i}')
layer.eval()
for param in layer.parameters():
param.requires_grad = False
[docs] def train(self, mode=True):
super(MobileNetV3, self).train(mode)
self._freeze_stages()
if mode and self.norm_eval:
for m in self.modules():
if isinstance(m, _BatchNorm):
m.eval()