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| # Copyright (c) OpenMMLab. All rights reserved. | |
| 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 | |
| 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 | |
| """ | |
| # Parameters to build layers. 4 parameters are needed to construct a | |
| # layer, from left to right: expand_ratio, channel, num_blocks, stride. | |
| 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(): | |
| # trick: eval have effect on BatchNorm only | |
| if isinstance(m, _BatchNorm): | |
| m.eval() | |