mmpose.models.backbones.mobilenet_v3 源代码
# Copyright (c) OpenMMLab. All rights reserved.
import copy
from mmcv.cnn import ConvModule
from torch.nn.modules.batchnorm import _BatchNorm
from mmpose.registry import MODELS
from .base_backbone import BaseBackbone
from .utils import InvertedResidual
[文档]@MODELS.register_module()
class MobileNetV3(BaseBackbone):
"""MobileNetV3 backbone.
Args:
arch (str): Architecture of mobilnetv3, from {small, big}.
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 (None or Sequence[int]): Output from which stages.
Default: (-1, ), which means output tensors from final stage.
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.
init_cfg (dict or list[dict], optional): Initialization config dict.
Default:
``[
dict(type='Kaiming', layer=['Conv2d']),
dict(
type='Constant',
val=1,
layer=['_BatchNorm'])
]``
"""
# 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],
[3, 72, 24, False, 'ReLU', 2],
[3, 88, 24, False, 'ReLU', 1],
[5, 96, 40, True, 'HSwish', 2],
[5, 240, 40, True, 'HSwish', 1],
[5, 240, 40, True, 'HSwish', 1],
[5, 120, 48, True, 'HSwish', 1],
[5, 144, 48, True, 'HSwish', 1],
[5, 288, 96, True, 'HSwish', 2],
[5, 576, 96, True, 'HSwish', 1],
[5, 576, 96, True, 'HSwish', 1]],
'big': [[3, 16, 16, False, 'ReLU', 1],
[3, 64, 24, False, 'ReLU', 2],
[3, 72, 24, False, 'ReLU', 1],
[5, 72, 40, True, 'ReLU', 2],
[5, 120, 40, True, 'ReLU', 1],
[5, 120, 40, True, 'ReLU', 1],
[3, 240, 80, False, 'HSwish', 2],
[3, 200, 80, False, 'HSwish', 1],
[3, 184, 80, False, 'HSwish', 1],
[3, 184, 80, False, 'HSwish', 1],
[3, 480, 112, True, 'HSwish', 1],
[3, 672, 112, True, 'HSwish', 1],
[5, 672, 160, True, 'HSwish', 1],
[5, 672, 160, True, 'HSwish', 2],
[5, 960, 160, True, 'HSwish', 1]]
} # yapf: disable
def __init__(self,
arch='small',
conv_cfg=None,
norm_cfg=dict(type='BN'),
out_indices=(-1, ),
frozen_stages=-1,
norm_eval=False,
with_cp=False,
init_cfg=[
dict(type='Kaiming', layer=['Conv2d']),
dict(type='Constant', val=1, layer=['_BatchNorm'])
]):
# Protect mutable default arguments
norm_cfg = copy.deepcopy(norm_cfg)
super().__init__(init_cfg=init_cfg)
assert arch in self.arch_settings
for index in out_indices:
if index not in range(-len(self.arch_settings[arch]),
len(self.arch_settings[arch])):
raise ValueError('the item in out_indices must in '
f'range(0, {len(self.arch_settings[arch])}). '
f'But received {index}')
if frozen_stages not in range(-1, len(self.arch_settings[arch])):
raise ValueError('frozen_stages must be in range(-1, '
f'{len(self.arch_settings[arch])}). '
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.norm_eval = norm_eval
self.with_cp = with_cp
self.in_channels = 16
self.conv1 = ConvModule(
in_channels=3,
out_channels=self.in_channels,
kernel_size=3,
stride=2,
padding=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=dict(type='HSwish'))
self.layers = self._make_layer()
self.feat_dim = self.arch_settings[arch][-1][2]
def _make_layer(self):
layers = []
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 with_se:
se_cfg = dict(
channels=mid_channels,
ratio=4,
act_cfg=(dict(type='ReLU'),
dict(type='HSigmoid', bias=1.0, divisor=2.0)))
else:
se_cfg = None
layer = InvertedResidual(
in_channels=self.in_channels,
out_channels=out_channels,
mid_channels=mid_channels,
kernel_size=kernel_size,
stride=stride,
se_cfg=se_cfg,
with_expand_conv=True,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=dict(type=act),
with_cp=self.with_cp)
self.in_channels = out_channels
layer_name = f'layer{i + 1}'
self.add_module(layer_name, layer)
layers.append(layer_name)
return layers
[文档] def forward(self, x):
x = self.conv1(x)
outs = []
for i, layer_name in enumerate(self.layers):
layer = getattr(self, layer_name)
x = layer(x)
if i in self.out_indices or \
i - len(self.layers) in self.out_indices:
outs.append(x)
return tuple(outs)
def _freeze_stages(self):
if self.frozen_stages >= 0:
for param in self.conv1.parameters():
param.requires_grad = False
for i in range(1, self.frozen_stages + 1):
layer = getattr(self, f'layer{i}')
layer.eval()
for param in layer.parameters():
param.requires_grad = False
[文档] def train(self, mode=True):
super().train(mode)
self._freeze_stages()
if mode and self.norm_eval:
for m in self.modules():
if isinstance(m, _BatchNorm):
m.eval()