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| # 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 | |
| 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() | |