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| # Copyright (c) OpenMMLab. All rights reserved. | |
| import torch.nn as nn | |
| import torch.utils.checkpoint as cp | |
| from mmcv.cnn import ConvModule | |
| from mmcv.cnn.bricks import DropPath | |
| from mmengine.model import BaseModule | |
| from .se_layer import SELayer | |
| class InvertedResidual(BaseModule): | |
| """Inverted Residual Block. | |
| Args: | |
| in_channels (int): The input channels of this module. | |
| out_channels (int): The output channels of this module. | |
| mid_channels (int): The input channels of the depthwise convolution. | |
| kernel_size (int): The kernel size of the depthwise convolution. | |
| Defaults to 3. | |
| stride (int): The stride of the depthwise convolution. Defaults to 1. | |
| se_cfg (dict, optional): Config dict for se layer. Defaults to None, | |
| which means no se layer. | |
| conv_cfg (dict): Config dict for convolution layer. Defaults to None, | |
| which means using conv2d. | |
| norm_cfg (dict): Config dict for normalization layer. | |
| Defaults to ``dict(type='BN')``. | |
| act_cfg (dict): Config dict for activation layer. | |
| Defaults to ``dict(type='ReLU')``. | |
| drop_path_rate (float): stochastic depth rate. Defaults to 0. | |
| with_cp (bool): Use checkpoint or not. Using checkpoint will save some | |
| memory while slowing down the training speed. Defaults to False. | |
| init_cfg (dict | list[dict], optional): Initialization config dict. | |
| """ | |
| def __init__(self, | |
| in_channels, | |
| out_channels, | |
| mid_channels, | |
| kernel_size=3, | |
| stride=1, | |
| se_cfg=None, | |
| conv_cfg=None, | |
| norm_cfg=dict(type='BN'), | |
| act_cfg=dict(type='ReLU'), | |
| drop_path_rate=0., | |
| with_cp=False, | |
| init_cfg=None): | |
| super(InvertedResidual, self).__init__(init_cfg) | |
| self.with_res_shortcut = (stride == 1 and in_channels == out_channels) | |
| assert stride in [1, 2] | |
| self.with_cp = with_cp | |
| self.drop_path = DropPath( | |
| drop_path_rate) if drop_path_rate > 0 else nn.Identity() | |
| self.with_se = se_cfg is not None | |
| self.with_expand_conv = (mid_channels != in_channels) | |
| if self.with_se: | |
| assert isinstance(se_cfg, dict) | |
| if self.with_expand_conv: | |
| self.expand_conv = ConvModule( | |
| in_channels=in_channels, | |
| out_channels=mid_channels, | |
| kernel_size=1, | |
| stride=1, | |
| padding=0, | |
| conv_cfg=conv_cfg, | |
| norm_cfg=norm_cfg, | |
| act_cfg=act_cfg) | |
| self.depthwise_conv = ConvModule( | |
| in_channels=mid_channels, | |
| out_channels=mid_channels, | |
| kernel_size=kernel_size, | |
| stride=stride, | |
| padding=kernel_size // 2, | |
| groups=mid_channels, | |
| conv_cfg=conv_cfg, | |
| norm_cfg=norm_cfg, | |
| act_cfg=act_cfg) | |
| if self.with_se: | |
| self.se = SELayer(**se_cfg) | |
| self.linear_conv = ConvModule( | |
| in_channels=mid_channels, | |
| out_channels=out_channels, | |
| kernel_size=1, | |
| stride=1, | |
| padding=0, | |
| conv_cfg=conv_cfg, | |
| norm_cfg=norm_cfg, | |
| act_cfg=None) | |
| def forward(self, x): | |
| """Forward function. | |
| Args: | |
| x (torch.Tensor): The input tensor. | |
| Returns: | |
| torch.Tensor: The output tensor. | |
| """ | |
| def _inner_forward(x): | |
| out = x | |
| if self.with_expand_conv: | |
| out = self.expand_conv(out) | |
| out = self.depthwise_conv(out) | |
| if self.with_se: | |
| out = self.se(out) | |
| out = self.linear_conv(out) | |
| if self.with_res_shortcut: | |
| return x + self.drop_path(out) | |
| else: | |
| return out | |
| if self.with_cp and x.requires_grad: | |
| out = cp.checkpoint(_inner_forward, x) | |
| else: | |
| out = _inner_forward(x) | |
| return out | |