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Source code for mmpretrain.models.backbones.mobilenet_v3

# Copyright (c) OpenMMLab. All rights reserved.
from mmcv.cnn import ConvModule
from torch.nn.modules.batchnorm import _BatchNorm

from mmpretrain.registry import MODELS
from ..utils import InvertedResidual
from .base_backbone import BaseBackbone


[docs]@MODELS.register_module() class MobileNetV3(BaseBackbone): """MobileNetV3 backbone. Args: arch (str): Architecture of mobilnetv3, from {small, large}. Default: small. conv_cfg (dict, optional): 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: None, 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]], 'small_075': [[3, 16, 16, True, 'ReLU', 2], [3, 72, 24, False, 'ReLU', 2], [3, 88, 24, False, 'ReLU', 1], [5, 96, 32, True, 'HSwish', 2], [5, 192, 32, True, 'HSwish', 1], [5, 192, 32, True, 'HSwish', 1], [5, 96, 40, True, 'HSwish', 1], [5, 120, 40, True, 'HSwish', 1], [5, 240, 72, True, 'HSwish', 2], [5, 432, 72, True, 'HSwish', 1], [5, 432, 72, True, 'HSwish', 1]], 'small_050': [[3, 16, 8, True, 'ReLU', 2], [3, 40, 16, False, 'ReLU', 2], [3, 56, 16, False, 'ReLU', 1], [5, 64, 24, True, 'HSwish', 2], [5, 144, 24, True, 'HSwish', 1], [5, 144, 24, True, 'HSwish', 1], [5, 72, 24, True, 'HSwish', 1], [5, 72, 24, True, 'HSwish', 1], [5, 144, 48, True, 'HSwish', 2], [5, 288, 48, True, 'HSwish', 1], [5, 288, 48, True, 'HSwish', 1]], 'large': [[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', 2], [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', eps=0.001, momentum=0.01), out_indices=None, frozen_stages=-1, norm_eval=False, with_cp=False, init_cfg=[ dict( type='Kaiming', layer=['Conv2d'], nonlinearity='leaky_relu'), dict(type='Normal', layer=['Linear'], std=0.01), dict(type='Constant', layer=['BatchNorm2d'], val=1) ]): super(MobileNetV3, self).__init__(init_cfg) assert arch in self.arch_settings if out_indices is None: out_indices = (12, ) if 'small' in arch else (16, ) for order, index in enumerate(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.norm_eval = norm_eval self.with_cp = with_cp self.layers = self._make_layer() self.feat_dim = self.arch_settings[arch][-1][1] def _make_layer(self): layers = [] layer_setting = self.arch_settings[self.arch] in_channels = 16 layer = ConvModule( in_channels=3, out_channels=in_channels, kernel_size=3, stride=2, padding=1, conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg, act_cfg=dict(type='HSwish')) self.add_module('layer0', layer) layers.append('layer0') 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=3, divisor=6, min_value=0, max_value=1))) 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, 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 before pooling # TODO: No dilation layer = ConvModule( in_channels=in_channels, out_channels=mid_channels, kernel_size=1, stride=1, 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) return layers 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 tuple(outs) def _freeze_stages(self): for i in range(0, 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(MobileNetV3, self).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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