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| # 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 | |
| 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'))) | |
| 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 | |
| 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') | |
| 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 | |
| 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() | |