Source code for mmrazor.models.ops.mobilenet_series

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

from ..builder import OPS
from .base import BaseOP


@OPS.register_module()
class MBBlock(BaseOP):
    """Mobilenet block for Searchable backbone.

    Args:
        kernel_size (int): Size of the convolving kernel.
        expand_ratio (int): The input channels' expand factor of the depthwise
             convolution.
        se_cfg (dict, optional): Config dict for se layer. Defaults to None,
            which means no se layer.
        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='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. Default: False.

    Returns:
        Tensor: The output tensor.
    """

    def __init__(self,
                 kernel_size,
                 expand_ratio,
                 se_cfg=None,
                 conv_cfg=None,
                 norm_cfg=dict(type='BN'),
                 act_cfg=dict(type='ReLU'),
                 drop_path_rate=0.,
                 with_cp=False,
                 **kwargs):

        super(MBBlock, self).__init__(**kwargs)
        self.with_res_shortcut = (
            self.stride == 1 and self.in_channels == self.out_channels)
        assert self.stride in [1, 2]
        self.kernel_size = kernel_size
        self.conv_cfg = conv_cfg
        self.norm_cfg = norm_cfg
        self.act_cfg = act_cfg
        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.mid_channels = self.in_channels * expand_ratio
        self.with_expand_conv = (self.mid_channels != self.in_channels)

        if self.with_se:
            assert isinstance(se_cfg, dict)

        if self.with_expand_conv:
            self.expand_conv = ConvModule(
                in_channels=self.in_channels,
                out_channels=self.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=self.mid_channels,
            out_channels=self.mid_channels,
            kernel_size=kernel_size,
            stride=self.stride,
            padding=kernel_size // 2,
            groups=self.mid_channels,
            conv_cfg=conv_cfg,
            norm_cfg=norm_cfg,
            act_cfg=act_cfg)
        if self.with_se:
            self.se = SELayer(self.mid_channels, **se_cfg)
        self.linear_conv = ConvModule(
            in_channels=self.mid_channels,
            out_channels=self.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