| |
| import warnings |
|
|
| import torch.nn as nn |
| from mmcv.cnn import ConvModule |
| from mmengine.model import BaseModule |
| from torch.nn.modules.batchnorm import _BatchNorm |
|
|
| from mmdet.registry import MODELS |
| from ..layers import InvertedResidual |
| from ..utils import make_divisible |
|
|
|
|
| @MODELS.register_module() |
| class MobileNetV2(BaseModule): |
| """MobileNetV2 backbone. |
| |
| Args: |
| widen_factor (float): Width multiplier, multiply number of |
| channels in each layer by this amount. Default: 1.0. |
| out_indices (Sequence[int], optional): Output from which stages. |
| Default: (1, 2, 4, 7). |
| frozen_stages (int): Stages to be frozen (all param fixed). |
| Default: -1, which means not freezing any parameters. |
| 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'). |
| 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. |
| pretrained (str, optional): model pretrained path. Default: None |
| init_cfg (dict or list[dict], optional): Initialization config dict. |
| Default: None |
| """ |
|
|
| |
| |
| arch_settings = [[1, 16, 1, 1], [6, 24, 2, 2], [6, 32, 3, 2], |
| [6, 64, 4, 2], [6, 96, 3, 1], [6, 160, 3, 2], |
| [6, 320, 1, 1]] |
|
|
| def __init__(self, |
| widen_factor=1., |
| out_indices=(1, 2, 4, 7), |
| frozen_stages=-1, |
| conv_cfg=None, |
| norm_cfg=dict(type='BN'), |
| act_cfg=dict(type='ReLU6'), |
| norm_eval=False, |
| with_cp=False, |
| pretrained=None, |
| init_cfg=None): |
| super(MobileNetV2, self).__init__(init_cfg) |
|
|
| self.pretrained = pretrained |
| assert not (init_cfg and pretrained), \ |
| 'init_cfg and pretrained cannot be specified at the same time' |
| if isinstance(pretrained, str): |
| warnings.warn('DeprecationWarning: pretrained is deprecated, ' |
| 'please use "init_cfg" instead') |
| self.init_cfg = dict(type='Pretrained', checkpoint=pretrained) |
| elif pretrained is None: |
| if init_cfg is None: |
| self.init_cfg = [ |
| dict(type='Kaiming', layer='Conv2d'), |
| dict( |
| type='Constant', |
| val=1, |
| layer=['_BatchNorm', 'GroupNorm']) |
| ] |
| else: |
| raise TypeError('pretrained must be a str or None') |
|
|
| self.widen_factor = widen_factor |
| self.out_indices = out_indices |
| if not set(out_indices).issubset(set(range(0, 8))): |
| raise ValueError('out_indices must be a subset of range' |
| f'(0, 8). But received {out_indices}') |
|
|
| if frozen_stages not in range(-1, 8): |
| raise ValueError('frozen_stages must be in range(-1, 8). ' |
| 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, stride = layer_cfg |
| out_channels = make_divisible(channel * widen_factor, 8) |
| inverted_res_layer = self.make_layer( |
| out_channels=out_channels, |
| num_blocks=num_blocks, |
| stride=stride, |
| expand_ratio=expand_ratio) |
| layer_name = f'layer{i + 1}' |
| self.add_module(layer_name, inverted_res_layer) |
| self.layers.append(layer_name) |
|
|
| if widen_factor > 1.0: |
| self.out_channel = int(1280 * widen_factor) |
| else: |
| self.out_channel = 1280 |
|
|
| layer = ConvModule( |
| in_channels=self.in_channels, |
| out_channels=self.out_channel, |
| kernel_size=1, |
| stride=1, |
| padding=0, |
| conv_cfg=self.conv_cfg, |
| norm_cfg=self.norm_cfg, |
| act_cfg=self.act_cfg) |
| self.add_module('conv2', layer) |
| self.layers.append('conv2') |
|
|
| def make_layer(self, out_channels, num_blocks, stride, 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. Default: 1 |
| expand_ratio (int): Expand the number of channels of the |
| hidden layer in InvertedResidual by this ratio. Default: 6. |
| """ |
| layers = [] |
| for i in range(num_blocks): |
| if i >= 1: |
| stride = 1 |
| layers.append( |
| InvertedResidual( |
| self.in_channels, |
| out_channels, |
| mid_channels=int(round(self.in_channels * expand_ratio)), |
| stride=stride, |
| with_expand_conv=expand_ratio != 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 _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 forward(self, x): |
| """Forward function.""" |
| 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) |
| return tuple(outs) |
|
|
| def train(self, mode=True): |
| """Convert the model into training mode while keep normalization layer |
| frozen.""" |
| 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() |
|
|
