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# Source code for mmseg.models.backbones.mobilenet_v3

    import logging

    import mmcv
    import torch.nn as nn
    from mmcv.cnn import ConvModule, constant_init, kaiming_init
    from mmcv.cnn.bricks import Conv2dAdaptivePadding
    from mmcv.runner import load_checkpoint
    from torch.nn.modules.batchnorm import _BatchNorm

    from ..builder import BACKBONES
    from ..utils import InvertedResidualV3 as InvertedResidual

    @BACKBONES.register_module()
    class MobileNetV3(nn.Module):
        """MobileNetV3 backbone.

        This backbone is the improved implementation of `Searching for MobileNetV3
        <https://ieeexplore.ieee.org/document/9008835>`_.

        Args:
            arch (str): Architechture of mobilnetv3, from {'small', 'large'}.
                Default: 'small'.
            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').
            out_indices (tuple[int]): Output from which layer.
                Default: (0, 1, 12).
            frozen_stages (int): Stages to be frozen (all param fixed).
                Defualt: -1, which means not freezing any parameters.
            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.
                Defualt: False.
        """
        # Parameters to build each block:
        #     [kernel size, mid channels, out channels, with_se, act type, stride]
        arch_settings = {
            'small': [[3, 16, 16, True, 'ReLU', 2],  # block0 layer1 os=4
                      [3, 72, 24, False, 'ReLU', 2],  # block1 layer2 os=8
                      [3, 88, 24, False, 'ReLU', 1],
                      [5, 96, 40, True, 'HSwish', 2],  # block2 layer4 os=16
                      [5, 240, 40, True, 'HSwish', 1],
                      [5, 240, 40, True, 'HSwish', 1],
                      [5, 120, 48, True, 'HSwish', 1],  # block3 layer7 os=16
                      [5, 144, 48, True, 'HSwish', 1],
                      [5, 288, 96, True, 'HSwish', 2],  # block4 layer9 os=32
                      [5, 576, 96, True, 'HSwish', 1],
                      [5, 576, 96, True, 'HSwish', 1]],
            'large': [[3, 16, 16, False, 'ReLU', 1],  # block0 layer1 os=2
                      [3, 64, 24, False, 'ReLU', 2],  # block1 layer2 os=4
                      [3, 72, 24, False, 'ReLU', 1],
                      [5, 72, 40, True, 'ReLU', 2],  # block2 layer4 os=8
                      [5, 120, 40, True, 'ReLU', 1],
                      [5, 120, 40, True, 'ReLU', 1],
                      [3, 240, 80, False, 'HSwish', 2],  # block3 layer7 os=16
                      [3, 200, 80, False, 'HSwish', 1],
                      [3, 184, 80, False, 'HSwish', 1],
                      [3, 184, 80, False, 'HSwish', 1],
                      [3, 480, 112, True, 'HSwish', 1],  # block4 layer11 os=16
                      [3, 672, 112, True, 'HSwish', 1],
                      [5, 672, 160, True, 'HSwish', 2],  # block5 layer13 os=32
                      [5, 960, 160, True, 'HSwish', 1],
                      [5, 960, 160, True, 'HSwish', 1]]
        }  # yapf: disable

        def __init__(self,
                     arch='small',
                     conv_cfg=None,
                     norm_cfg=dict(type='BN'),
                     out_indices=(0, 1, 12),
                     frozen_stages=-1,
                     reduction_factor=1,
                     norm_eval=False,
                     with_cp=False):
            super(MobileNetV3, self).__init__()
            assert arch in self.arch_settings
            assert isinstance(reduction_factor, int) and reduction_factor > 0
            assert mmcv.is_tuple_of(out_indices, int)
            for index in out_indices:
                if index not in range(0, len(self.arch_settings[arch]) + 2):
                    raise ValueError(
                        'the item in out_indices must in '
                        f'range(0, {len(self.arch_settings[arch])+2}). '
                        f'But received {index}')

            if frozen_stages not in range(-1, len(self.arch_settings[arch]) + 2):
                raise ValueError('frozen_stages must be in range(-1, '
                                 f'{len(self.arch_settings[arch])+2}). '
                                 f'But received {frozen_stages}')
            self.arch = arch
            self.conv_cfg = conv_cfg
            self.norm_cfg = norm_cfg
            self.out_indices = out_indices
            self.frozen_stages = frozen_stages
            self.reduction_factor = reduction_factor
            self.norm_eval = norm_eval
            self.with_cp = with_cp
            self.layers = self._make_layer()

        def _make_layer(self):
            layers = []

            # build the first layer (layer0)
            in_channels = 16
            layer = ConvModule(
                in_channels=3,
                out_channels=in_channels,
                kernel_size=3,
                stride=2,
                padding=1,
                conv_cfg=dict(type='Conv2dAdaptivePadding'),
                norm_cfg=self.norm_cfg,
                act_cfg=dict(type='HSwish'))
            self.add_module('layer0', layer)
            layers.append('layer0')

            layer_setting = self.arch_settings[self.arch]
            for i, params in enumerate(layer_setting):
                (kernel_size, mid_channels, out_channels, with_se, act,
                 stride) = params

                if self.arch == 'large' and i >= 12 or self.arch == 'small' and \
                        i >= 8:
                    mid_channels = mid_channels // self.reduction_factor
                    out_channels = out_channels // self.reduction_factor

                if with_se:
                    se_cfg = dict(
                        channels=mid_channels,
                        ratio=4,
                        act_cfg=(dict(type='ReLU'),
                                 dict(type='HSigmoid', bias=3.0, divisor=6.0)))
                else:
                    se_cfg = None

                layer = InvertedResidual(
                    in_channels=in_channels,
                    out_channels=out_channels,
                    mid_channels=mid_channels,
                    kernel_size=kernel_size,
                    stride=stride,
                    se_cfg=se_cfg,
                    with_expand_conv=(in_channels != mid_channels),
                    conv_cfg=self.conv_cfg,
                    norm_cfg=self.norm_cfg,
                    act_cfg=dict(type=act),
                    with_cp=self.with_cp)
                in_channels = out_channels
                layer_name = 'layer{}'.format(i + 1)
                self.add_module(layer_name, layer)
                layers.append(layer_name)

            # build the last layer
            # block5 layer12 os=32 for small model
            # block6 layer16 os=32 for large model
            layer = ConvModule(
                in_channels=in_channels,
                out_channels=576 if self.arch == 'small' else 960,
                kernel_size=1,
                stride=1,
                dilation=4,
                padding=0,
                conv_cfg=self.conv_cfg,
                norm_cfg=self.norm_cfg,
                act_cfg=dict(type='HSwish'))
            layer_name = 'layer{}'.format(len(layer_setting) + 1)
            self.add_module(layer_name, layer)
            layers.append(layer_name)

            # next, convert backbone MobileNetV3 to a semantic segmentation version
            if self.arch == 'small':
                self.layer4.depthwise_conv.conv.stride = (1, 1)
                self.layer9.depthwise_conv.conv.stride = (1, 1)
                for i in range(4, len(layers)):
                    layer = getattr(self, layers[i])
                    if isinstance(layer, InvertedResidual):
                        modified_module = layer.depthwise_conv.conv
                    else:
                        modified_module = layer.conv

                    if i < 9:
                        modified_module.dilation = (2, 2)
                        pad = 2
                    else:
                        modified_module.dilation = (4, 4)
                        pad = 4

                    if not isinstance(modified_module, Conv2dAdaptivePadding):
                        # Adjust padding
                        pad *= (modified_module.kernel_size[0] - 1) // 2
                        modified_module.padding = (pad, pad)
            else:
                self.layer7.depthwise_conv.conv.stride = (1, 1)
                self.layer13.depthwise_conv.conv.stride = (1, 1)
                for i in range(7, len(layers)):
                    layer = getattr(self, layers[i])
                    if isinstance(layer, InvertedResidual):
                        modified_module = layer.depthwise_conv.conv
                    else:
                        modified_module = layer.conv

                    if i < 13:
                        modified_module.dilation = (2, 2)
                        pad = 2
                    else:
                        modified_module.dilation = (4, 4)
                        pad = 4

                    if not isinstance(modified_module, Conv2dAdaptivePadding):
                        # Adjust padding
                        pad *= (modified_module.kernel_size[0] - 1) // 2
                        modified_module.padding = (pad, pad)

            return layers

        def init_weights(self, pretrained=None):
            if isinstance(pretrained, str):
                logger = logging.getLogger()
                load_checkpoint(self, pretrained, strict=False, logger=logger)
            elif pretrained is None:
                for m in self.modules():
                    if isinstance(m, nn.Conv2d):
                        kaiming_init(m)
                    elif isinstance(m, nn.BatchNorm2d):
                        constant_init(m, 1)
            else:
                raise TypeError('pretrained must be a str or None')

        def forward(self, 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 outs

        def _freeze_stages(self):
            for i in range(self.frozen_stages + 1):
                layer = getattr(self, f'layer{i}')
                layer.eval()
                for param in layer.parameters():
                    param.requires_grad = False

        def train(self, mode=True):
            super(MobileNetV3, self).train(mode)
            self._freeze_stages()
            if mode and self.norm_eval:
                for m in self.modules():
                    if isinstance(m, _BatchNorm):
                        m.eval()