head_extractor/src/mmseg/models/utils/inverted_residual.py

# Copyright (c) OpenMMLab. All rights reserved.
from mmcv.cnn import ConvModule
from torch import nn
from torch.utils import checkpoint as cp

from .se_layer import SELayer


class InvertedResidual(nn.Module):
    """InvertedResidual block for MobileNetV2.

    Args:
        in_channels (int): The input channels of the InvertedResidual block.
        out_channels (int): The output channels of the InvertedResidual block.
        stride (int): Stride of the middle (first) 3x3 convolution.
        expand_ratio (int): Adjusts number of channels of the hidden layer
            in InvertedResidual by this amount.
        dilation (int): Dilation rate of depthwise conv. Default: 1
        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').
        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
            memory while slowing down the training speed. Default: False.

    Returns:
        Tensor: The output tensor.
    """

    def __init__(self,
                 in_channels,
                 out_channels,
                 stride,
                 expand_ratio,
                 dilation=1,
                 conv_cfg=None,
                 norm_cfg=dict(type='BN'),
                 act_cfg=dict(type='ReLU6'),
                 with_cp=False,
                 **kwargs):
        super().__init__()
        self.stride = stride
        assert stride in [1, 2], f'stride must in [1, 2]. ' \
            f'But received {stride}.'
        self.with_cp = with_cp
        self.use_res_connect = self.stride == 1 and in_channels == out_channels
        hidden_dim = int(round(in_channels * expand_ratio))

        layers = []
        if expand_ratio != 1:
            layers.append(
                ConvModule(
                    in_channels=in_channels,
                    out_channels=hidden_dim,
                    kernel_size=1,
                    conv_cfg=conv_cfg,
                    norm_cfg=norm_cfg,
                    act_cfg=act_cfg,
                    **kwargs))
        layers.extend([
            ConvModule(
                in_channels=hidden_dim,
                out_channels=hidden_dim,
                kernel_size=3,
                stride=stride,
                padding=dilation,
                dilation=dilation,
                groups=hidden_dim,
                conv_cfg=conv_cfg,
                norm_cfg=norm_cfg,
                act_cfg=act_cfg,
                **kwargs),
            ConvModule(
                in_channels=hidden_dim,
                out_channels=out_channels,
                kernel_size=1,
                conv_cfg=conv_cfg,
                norm_cfg=norm_cfg,
                act_cfg=None,
                **kwargs)
        ])
        self.conv = nn.Sequential(*layers)

    def forward(self, x):

        def _inner_forward(x):
            if self.use_res_connect:
                return x + self.conv(x)
            else:
                return self.conv(x)

        if self.with_cp and x.requires_grad:
            out = cp.checkpoint(_inner_forward, x)
        else:
            out = _inner_forward(x)

        return out


class InvertedResidualV3(nn.Module):
    """Inverted Residual Block for MobileNetV3.

    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.
            Default: 3.
        stride (int): The stride of the depthwise convolution. Default: 1.
        se_cfg (dict): Config dict for se layer. Default: None, which means no
            se layer.
        with_expand_conv (bool): Use expand conv or not. If set False,
            mid_channels must be the same with in_channels. Default: True.
        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='ReLU').
        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
            memory while slowing down the training speed. Default: False.

    Returns:
        Tensor: The output tensor.
    """

    def __init__(self,
                 in_channels,
                 out_channels,
                 mid_channels,
                 kernel_size=3,
                 stride=1,
                 se_cfg=None,
                 with_expand_conv=True,
                 conv_cfg=None,
                 norm_cfg=dict(type='BN'),
                 act_cfg=dict(type='ReLU'),
                 with_cp=False):
        super().__init__()
        self.with_res_shortcut = (stride == 1 and in_channels == out_channels)
        assert stride in [1, 2]
        self.with_cp = with_cp
        self.with_se = se_cfg is not None
        self.with_expand_conv = with_expand_conv

        if self.with_se:
            assert isinstance(se_cfg, dict)
        if not self.with_expand_conv:
            assert mid_channels == in_channels

        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=dict(
                type='Conv2dAdaptivePadding') if stride == 2 else 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):

        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 + 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