File size: 6,586 Bytes
2de1f98 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 |
# Copyright (c) OpenMMLab. All rights reserved.
import copy
import logging
import torch.nn as nn
from mmcv.cnn import ConvModule
from torch.nn.modules.batchnorm import _BatchNorm
from ..builder import BACKBONES
from .base_backbone import BaseBackbone
from .utils import InvertedResidual, load_checkpoint
@BACKBONES.register_module()
class ViPNAS_MobileNetV3(BaseBackbone):
"""ViPNAS_MobileNetV3 backbone.
"ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search"
More details can be found in the `paper
<https://arxiv.org/abs/2105.10154>`__ .
Args:
wid (list(int)): Searched width config for each stage.
expan (list(int)): Searched expansion ratio config for each stage.
dep (list(int)): Searched depth config for each stage.
ks (list(int)): Searched kernel size config for each stage.
group (list(int)): Searched group number config for each stage.
att (list(bool)): Searched attention config for each stage.
stride (list(int)): Stride config for each stage.
act (list(dict)): Activation config for each stage.
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').
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.
"""
def __init__(self,
wid=[16, 16, 24, 40, 80, 112, 160],
expan=[None, 1, 5, 4, 5, 5, 6],
dep=[None, 1, 4, 4, 4, 4, 4],
ks=[3, 3, 7, 7, 5, 7, 5],
group=[None, 8, 120, 20, 100, 280, 240],
att=[None, True, True, False, True, True, True],
stride=[2, 1, 2, 2, 2, 1, 2],
act=[
'HSwish', 'ReLU', 'ReLU', 'ReLU', 'HSwish', 'HSwish',
'HSwish'
],
conv_cfg=None,
norm_cfg=dict(type='BN'),
frozen_stages=-1,
norm_eval=False,
with_cp=False):
# Protect mutable default arguments
norm_cfg = copy.deepcopy(norm_cfg)
super().__init__()
self.wid = wid
self.expan = expan
self.dep = dep
self.ks = ks
self.group = group
self.att = att
self.stride = stride
self.act = act
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.frozen_stages = frozen_stages
self.norm_eval = norm_eval
self.with_cp = with_cp
self.conv1 = ConvModule(
in_channels=3,
out_channels=self.wid[0],
kernel_size=self.ks[0],
stride=self.stride[0],
padding=self.ks[0] // 2,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=dict(type=self.act[0]))
self.layers = self._make_layer()
def _make_layer(self):
layers = []
layer_index = 0
for i, dep in enumerate(self.dep[1:]):
mid_channels = self.wid[i + 1] * self.expan[i + 1]
if self.att[i + 1]:
se_cfg = dict(
channels=mid_channels,
ratio=4,
act_cfg=(dict(type='ReLU'), dict(type='HSigmoid')))
else:
se_cfg = None
if self.expan[i + 1] == 1:
with_expand_conv = False
else:
with_expand_conv = True
for j in range(dep):
if j == 0:
stride = self.stride[i + 1]
in_channels = self.wid[i]
else:
stride = 1
in_channels = self.wid[i + 1]
layer = InvertedResidual(
in_channels=in_channels,
out_channels=self.wid[i + 1],
mid_channels=mid_channels,
kernel_size=self.ks[i + 1],
groups=self.group[i + 1],
stride=stride,
se_cfg=se_cfg,
with_expand_conv=with_expand_conv,
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=dict(type=self.act[i + 1]),
with_cp=self.with_cp)
layer_index += 1
layer_name = f'layer{layer_index}'
self.add_module(layer_name, layer)
layers.append(layer_name)
return layers
def init_weights(self, pretrained=None):
if isinstance(pretrained, str):
logger = logging.getLogger()
load_checkpoint(self, pretrained, strict=False, logger=logger)
elif pretrained is None:
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.normal_(m.weight, std=0.001)
for name, _ in m.named_parameters():
if name in ['bias']:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
else:
raise TypeError('pretrained must be a str or None')
def forward(self, x):
x = self.conv1(x)
for i, layer_name in enumerate(self.layers):
layer = getattr(self, layer_name)
x = layer(x)
return x
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
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()
|