Source code for mmpretrain.models.utils.inverted_residual

# 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