import logging import annotator.mmpkg.mmcv as mmcv import torch.nn as nn from annotator.mmpkg.mmcv.cnn import ConvModule, constant_init, kaiming_init from annotator.mmpkg.mmcv.cnn.bricks import Conv2dAdaptivePadding from annotator.mmpkg.mmcv.runner import load_checkpoint from torch.nn.modules.batchnorm import _BatchNorm from ..builder import BACKBONES from ..utils import InvertedResidualV3 as InvertedResidual @BACKBONES.register_module() class MobileNetV3(nn.Module): """MobileNetV3 backbone. This backbone is the improved implementation of `Searching for MobileNetV3 `_. Args: arch (str): Architecture of mobilnetv3, from {'small', 'large'}. 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 (tuple[int]): Output from which layer. Default: (0, 1, 12). 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], # block0 layer1 os=4 [3, 72, 24, False, 'ReLU', 2], # block1 layer2 os=8 [3, 88, 24, False, 'ReLU', 1], [5, 96, 40, True, 'HSwish', 2], # block2 layer4 os=16 [5, 240, 40, True, 'HSwish', 1], [5, 240, 40, True, 'HSwish', 1], [5, 120, 48, True, 'HSwish', 1], # block3 layer7 os=16 [5, 144, 48, True, 'HSwish', 1], [5, 288, 96, True, 'HSwish', 2], # block4 layer9 os=32 [5, 576, 96, True, 'HSwish', 1], [5, 576, 96, True, 'HSwish', 1]], 'large': [[3, 16, 16, False, 'ReLU', 1], # block0 layer1 os=2 [3, 64, 24, False, 'ReLU', 2], # block1 layer2 os=4 [3, 72, 24, False, 'ReLU', 1], [5, 72, 40, True, 'ReLU', 2], # block2 layer4 os=8 [5, 120, 40, True, 'ReLU', 1], [5, 120, 40, True, 'ReLU', 1], [3, 240, 80, False, 'HSwish', 2], # block3 layer7 os=16 [3, 200, 80, False, 'HSwish', 1], [3, 184, 80, False, 'HSwish', 1], [3, 184, 80, False, 'HSwish', 1], [3, 480, 112, True, 'HSwish', 1], # block4 layer11 os=16 [3, 672, 112, True, 'HSwish', 1], [5, 672, 160, True, 'HSwish', 2], # block5 layer13 os=32 [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'), out_indices=(0, 1, 12), frozen_stages=-1, reduction_factor=1, norm_eval=False, with_cp=False): super(MobileNetV3, self).__init__() assert arch in self.arch_settings assert isinstance(reduction_factor, int) and reduction_factor > 0 assert mmcv.is_tuple_of(out_indices, int) for index in 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.reduction_factor = reduction_factor self.norm_eval = norm_eval self.with_cp = with_cp self.layers = self._make_layer() def _make_layer(self): layers = [] # build the first layer (layer0) in_channels = 16 layer = ConvModule( in_channels=3, out_channels=in_channels, kernel_size=3, stride=2, padding=1, conv_cfg=dict(type='Conv2dAdaptivePadding'), norm_cfg=self.norm_cfg, act_cfg=dict(type='HSwish')) self.add_module('layer0', layer) layers.append('layer0') 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 self.arch == 'large' and i >= 12 or self.arch == 'small' and \ i >= 8: mid_channels = mid_channels // self.reduction_factor out_channels = out_channels // self.reduction_factor if with_se: se_cfg = dict( channels=mid_channels, ratio=4, act_cfg=(dict(type='ReLU'), dict(type='HSigmoid', bias=3.0, divisor=6.0))) 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, with_expand_conv=(in_channels != mid_channels), 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 # block5 layer12 os=32 for small model # block6 layer16 os=32 for large model layer = ConvModule( in_channels=in_channels, out_channels=576 if self.arch == 'small' else 960, kernel_size=1, stride=1, dilation=4, 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) # next, convert backbone MobileNetV3 to a semantic segmentation version if self.arch == 'small': self.layer4.depthwise_conv.conv.stride = (1, 1) self.layer9.depthwise_conv.conv.stride = (1, 1) for i in range(4, len(layers)): layer = getattr(self, layers[i]) if isinstance(layer, InvertedResidual): modified_module = layer.depthwise_conv.conv else: modified_module = layer.conv if i < 9: modified_module.dilation = (2, 2) pad = 2 else: modified_module.dilation = (4, 4) pad = 4 if not isinstance(modified_module, Conv2dAdaptivePadding): # Adjust padding pad *= (modified_module.kernel_size[0] - 1) // 2 modified_module.padding = (pad, pad) else: self.layer7.depthwise_conv.conv.stride = (1, 1) self.layer13.depthwise_conv.conv.stride = (1, 1) for i in range(7, len(layers)): layer = getattr(self, layers[i]) if isinstance(layer, InvertedResidual): modified_module = layer.depthwise_conv.conv else: modified_module = layer.conv if i < 13: modified_module.dilation = (2, 2) pad = 2 else: modified_module.dilation = (4, 4) pad = 4 if not isinstance(modified_module, Conv2dAdaptivePadding): # Adjust padding pad *= (modified_module.kernel_size[0] - 1) // 2 modified_module.padding = (pad, pad) 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): 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 outs def _freeze_stages(self): for i in range(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()