def __init__()

in easycv/models/backbones/hrnet.py [0:0]

• 90 lines of code
• 4 McCabe index (conditional complexity)

    def __init__(self,
                 arch='w32',
                 extra=None,
                 in_channels=3,
                 conv_cfg=None,
                 norm_cfg=dict(type='BN'),
                 norm_eval=False,
                 with_cp=False,
                 zero_init_residual=False,
                 multi_scale_output=False):
        # Protect mutable default arguments
        norm_cfg = copy.deepcopy(norm_cfg)
        super().__init__()

        extra = self.parse_arch(arch, extra)

        self.extra = extra
        self.conv_cfg = conv_cfg
        self.norm_cfg = norm_cfg
        self.norm_eval = norm_eval
        self.with_cp = with_cp
        self.zero_init_residual = zero_init_residual

        # stem net
        self.norm1_name, norm1 = build_norm_layer(self.norm_cfg, 64, postfix=1)
        self.norm2_name, norm2 = build_norm_layer(self.norm_cfg, 64, postfix=2)

        self.conv1 = build_conv_layer(
            self.conv_cfg,
            in_channels,
            64,
            kernel_size=3,
            stride=2,
            padding=1,
            bias=False)

        self.add_module(self.norm1_name, norm1)
        self.conv2 = build_conv_layer(
            self.conv_cfg,
            64,
            64,
            kernel_size=3,
            stride=2,
            padding=1,
            bias=False)

        self.add_module(self.norm2_name, norm2)
        self.relu = nn.ReLU(inplace=True)

        self.upsample_cfg = self.extra.get('upsample', {
            'mode': 'nearest',
            'align_corners': None
        })

        # stage 1
        self.stage1_cfg = self.extra['stage1']
        num_channels = self.stage1_cfg['num_channels'][0]
        block_type = self.stage1_cfg['block']
        num_blocks = self.stage1_cfg['num_blocks'][0]

        block = self.blocks_dict[block_type]
        stage1_out_channels = num_channels * get_expansion(block)
        self.layer1 = self._make_layer(block, 64, stage1_out_channels,
                                       num_blocks)

        # stage 2
        self.stage2_cfg = self.extra['stage2']
        num_channels = self.stage2_cfg['num_channels']
        block_type = self.stage2_cfg['block']

        block = self.blocks_dict[block_type]
        num_channels = [
            channel * get_expansion(block) for channel in num_channels
        ]
        self.transition1 = self._make_transition_layer([stage1_out_channels],
                                                       num_channels)
        self.stage2, pre_stage_channels = self._make_stage(
            self.stage2_cfg, num_channels)

        # stage 3
        self.stage3_cfg = self.extra['stage3']
        num_channels = self.stage3_cfg['num_channels']
        block_type = self.stage3_cfg['block']

        block = self.blocks_dict[block_type]
        num_channels = [
            channel * get_expansion(block) for channel in num_channels
        ]
        self.transition2 = self._make_transition_layer(pre_stage_channels,
                                                       num_channels)
        self.stage3, pre_stage_channels = self._make_stage(
            self.stage3_cfg, num_channels)

        # stage 4
        self.stage4_cfg = self.extra['stage4']
        num_channels = self.stage4_cfg['num_channels']
        block_type = self.stage4_cfg['block']

        block = self.blocks_dict[block_type]
        num_channels = [
            channel * get_expansion(block) for channel in num_channels
        ]
        self.transition3 = self._make_transition_layer(pre_stage_channels,
                                                       num_channels)

        self.stage4, pre_stage_channels = self._make_stage(
            self.stage4_cfg,
            num_channels,
            multiscale_output=self.stage4_cfg.get('multiscale_output',
                                                  multi_scale_output))

        self.default_pretrained_model_path = model_urls.get(
            self.__class__.__name__ + arch, None)