def __init__()

in easycv/models/pose/heads/topdown_heatmap_simple_head.py [0:0]

• 82 lines of code
• 17 McCabe index (conditional complexity)

    def __init__(self,
                 in_channels,
                 out_channels,
                 num_deconv_layers=3,
                 num_deconv_filters=(256, 256, 256),
                 num_deconv_kernels=(4, 4, 4),
                 extra=None,
                 in_index=0,
                 input_transform=None,
                 align_corners=False,
                 loss_keypoint=None,
                 train_cfg=None,
                 test_cfg=None):
        super().__init__()

        self.in_channels = in_channels
        self.loss = build_loss(loss_keypoint)

        self.train_cfg = {} if train_cfg is None else train_cfg
        self.test_cfg = {} if test_cfg is None else test_cfg
        self.target_type = self.test_cfg.get('target_type', 'GaussianHeatmap')

        self._init_inputs(in_channels, in_index, input_transform)
        self.in_index = in_index
        self.align_corners = align_corners

        if extra is not None and not isinstance(extra, dict):
            raise TypeError('extra should be dict or None.')

        if num_deconv_layers > 0:
            self.deconv_layers = self._make_deconv_layer(
                num_deconv_layers,
                num_deconv_filters,
                num_deconv_kernels,
            )
        elif num_deconv_layers == 0:
            self.deconv_layers = nn.Identity()
        else:
            raise ValueError(
                f'num_deconv_layers ({num_deconv_layers}) should >= 0.')

        identity_final_layer = False
        if extra is not None and 'final_conv_kernel' in extra:
            assert extra['final_conv_kernel'] in [0, 1, 3]
            if extra['final_conv_kernel'] == 3:
                padding = 1
            elif extra['final_conv_kernel'] == 1:
                padding = 0
            else:
                # 0 for Identity mapping.
                identity_final_layer = True
            kernel_size = extra['final_conv_kernel']
        else:
            kernel_size = 1
            padding = 0

        if identity_final_layer:
            self.final_layer = nn.Identity()
        else:
            conv_channels = num_deconv_filters[
                -1] if num_deconv_layers > 0 else self.in_channels

            layers = []
            if extra is not None:
                num_conv_layers = extra.get('num_conv_layers', 0)
                num_conv_kernels = extra.get('num_conv_kernels',
                                             [1] * num_conv_layers)

                for i in range(num_conv_layers):
                    layers.append(
                        build_conv_layer(
                            dict(type='Conv2d'),
                            in_channels=conv_channels,
                            out_channels=conv_channels,
                            kernel_size=num_conv_kernels[i],
                            stride=1,
                            padding=(num_conv_kernels[i] - 1) // 2))
                    layers.append(
                        build_norm_layer(dict(type='BN'), conv_channels)[1])
                    layers.append(nn.ReLU(inplace=True))

            layers.append(
                build_conv_layer(
                    cfg=dict(type='Conv2d'),
                    in_channels=conv_channels,
                    out_channels=out_channels,
                    kernel_size=kernel_size,
                    stride=1,
                    padding=padding))

            if len(layers) > 1:
                self.final_layer = nn.Sequential(*layers)
            else:
                self.final_layer = layers[0]