def __init__()

in maskrcnn_benchmark/modeling/roi_heads/box_head/roi_box_feature_extractors.py [0:0]

• 43 lines of code
• 8 McCabe index (conditional complexity)

    def __init__(self, cfg, in_channels):
        super(FPNXconv1fcFeatureExtractor, self).__init__()

        resolution = cfg.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION
        scales = cfg.MODEL.ROI_BOX_HEAD.POOLER_SCALES
        sampling_ratio = cfg.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO
        pooler = Pooler(
            output_size=(resolution, resolution),
            scales=scales,
            sampling_ratio=sampling_ratio,
        )
        self.pooler = pooler

        use_gn = cfg.MODEL.ROI_BOX_HEAD.USE_GN
        conv_head_dim = cfg.MODEL.ROI_BOX_HEAD.CONV_HEAD_DIM
        num_stacked_convs = cfg.MODEL.ROI_BOX_HEAD.NUM_STACKED_CONVS
        dilation = cfg.MODEL.ROI_BOX_HEAD.DILATION

        xconvs = []
        for ix in range(num_stacked_convs):
            xconvs.append(
                nn.Conv2d(
                    in_channels,
                    conv_head_dim,
                    kernel_size=3,
                    stride=1,
                    padding=dilation,
                    dilation=dilation,
                    bias=False if use_gn else True
                )
            )
            in_channels = conv_head_dim
            if use_gn:
                xconvs.append(group_norm(in_channels))
            xconvs.append(nn.ReLU(inplace=True))

        self.add_module("xconvs", nn.Sequential(*xconvs))
        for modules in [self.xconvs,]:
            for l in modules.modules():
                if isinstance(l, nn.Conv2d):
                    torch.nn.init.normal_(l.weight, std=0.01)
                    if not use_gn:
                        torch.nn.init.constant_(l.bias, 0)

        input_size = conv_head_dim * resolution ** 2
        representation_size = cfg.MODEL.ROI_BOX_HEAD.MLP_HEAD_DIM
        self.fc6 = make_fc(input_size, representation_size, use_gn=False)
        self.out_channels = representation_size