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Source code for mmpose.models.backbones.mobilenet_v3

# Copyright (c) OpenMMLab. All rights reserved.
import copy
import logging

import torch.nn as nn
from mmcv.cnn import ConvModule, constant_init, kaiming_init
from torch.nn.modules.batchnorm import _BatchNorm

from ..builder import BACKBONES
from .base_backbone import BaseBackbone
from .utils import InvertedResidual, load_checkpoint


[docs]@BACKBONES.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. """ # 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): # Protect mutable default arguments norm_cfg = copy.deepcopy(norm_cfg) super().__init__() 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
[docs] 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): kaiming_init(m) elif isinstance(m, nn.BatchNorm2d): constant_init(m, 1) else: raise TypeError('pretrained must be a str or None')
[docs] 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) if len(outs) == 1: return outs[0] 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
[docs] 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()
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