| import logging |
|
|
| import torch.nn as nn |
| from annotator.mmpkg.mmcv.cnn import ConvModule, constant_init, kaiming_init |
| from annotator.mmpkg.mmcv.runner import load_checkpoint |
| from torch.nn.modules.batchnorm import _BatchNorm |
|
|
| from ..builder import BACKBONES |
| from ..utils import InvertedResidual, make_divisible |
|
|
|
|
| @BACKBONES.register_module() |
| class MobileNetV2(nn.Module): |
| """MobileNetV2 backbone. |
| |
| Args: |
| widen_factor (float): Width multiplier, multiply number of |
| channels in each layer by this amount. Default: 1.0. |
| strides (Sequence[int], optional): Strides of the first block of each |
| layer. If not specified, default config in ``arch_setting`` will |
| be used. |
| dilations (Sequence[int]): Dilation of each layer. |
| out_indices (None or Sequence[int]): Output from which stages. |
| Default: (7, ). |
| frozen_stages (int): Stages to be frozen (all param fixed). |
| Default: -1, which means not freezing any parameters. |
| 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'). |
| act_cfg (dict): Config dict for activation layer. |
| Default: dict(type='ReLU6'). |
| 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. |
| """ |
|
|
| |
| |
| arch_settings = [[1, 16, 1], [6, 24, 2], [6, 32, 3], [6, 64, 4], |
| [6, 96, 3], [6, 160, 3], [6, 320, 1]] |
|
|
| def __init__(self, |
| widen_factor=1., |
| strides=(1, 2, 2, 2, 1, 2, 1), |
| dilations=(1, 1, 1, 1, 1, 1, 1), |
| out_indices=(1, 2, 4, 6), |
| frozen_stages=-1, |
| conv_cfg=None, |
| norm_cfg=dict(type='BN'), |
| act_cfg=dict(type='ReLU6'), |
| norm_eval=False, |
| with_cp=False): |
| super(MobileNetV2, self).__init__() |
| self.widen_factor = widen_factor |
| self.strides = strides |
| self.dilations = dilations |
| assert len(strides) == len(dilations) == len(self.arch_settings) |
| self.out_indices = out_indices |
| for index in out_indices: |
| if index not in range(0, 7): |
| raise ValueError('the item in out_indices must in ' |
| f'range(0, 8). But received {index}') |
|
|
| if frozen_stages not in range(-1, 7): |
| raise ValueError('frozen_stages must be in range(-1, 7). ' |
| f'But received {frozen_stages}') |
| self.out_indices = out_indices |
| self.frozen_stages = frozen_stages |
| self.conv_cfg = conv_cfg |
| self.norm_cfg = norm_cfg |
| self.act_cfg = act_cfg |
| self.norm_eval = norm_eval |
| self.with_cp = with_cp |
|
|
| self.in_channels = make_divisible(32 * widen_factor, 8) |
|
|
| self.conv1 = ConvModule( |
| in_channels=3, |
| out_channels=self.in_channels, |
| kernel_size=3, |
| stride=2, |
| padding=1, |
| conv_cfg=self.conv_cfg, |
| norm_cfg=self.norm_cfg, |
| act_cfg=self.act_cfg) |
|
|
| self.layers = [] |
|
|
| for i, layer_cfg in enumerate(self.arch_settings): |
| expand_ratio, channel, num_blocks = layer_cfg |
| stride = self.strides[i] |
| dilation = self.dilations[i] |
| out_channels = make_divisible(channel * widen_factor, 8) |
| inverted_res_layer = self.make_layer( |
| out_channels=out_channels, |
| num_blocks=num_blocks, |
| stride=stride, |
| dilation=dilation, |
| expand_ratio=expand_ratio) |
| layer_name = f'layer{i + 1}' |
| self.add_module(layer_name, inverted_res_layer) |
| self.layers.append(layer_name) |
|
|
| def make_layer(self, out_channels, num_blocks, stride, dilation, |
| expand_ratio): |
| """Stack InvertedResidual blocks to build a layer for MobileNetV2. |
| |
| Args: |
| out_channels (int): out_channels of block. |
| num_blocks (int): Number of blocks. |
| stride (int): Stride of the first block. |
| dilation (int): Dilation of the first block. |
| expand_ratio (int): Expand the number of channels of the |
| hidden layer in InvertedResidual by this ratio. |
| """ |
| layers = [] |
| for i in range(num_blocks): |
| layers.append( |
| InvertedResidual( |
| self.in_channels, |
| out_channels, |
| stride if i == 0 else 1, |
| expand_ratio=expand_ratio, |
| dilation=dilation if i == 0 else 1, |
| conv_cfg=self.conv_cfg, |
| norm_cfg=self.norm_cfg, |
| act_cfg=self.act_cfg, |
| with_cp=self.with_cp)) |
| self.in_channels = out_channels |
|
|
| return nn.Sequential(*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, (_BatchNorm, nn.GroupNorm)): |
| 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: |
| outs.append(x) |
|
|
| if len(outs) == 1: |
| return outs[0] |
| else: |
| 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(MobileNetV2, self).train(mode) |
| self._freeze_stages() |
| if mode and self.norm_eval: |
| for m in self.modules(): |
| if isinstance(m, _BatchNorm): |
| m.eval() |
|
|
