(self, cfg, in_channels)
Arguments:
            in_channels (int): number of channels of the input feature
Arguments:
            in_channels (int): number of channels of the input feature
Arguments : in_channels ( int ) : number of channels of the input feature
def __init__(self, cfg, in_channels):
        """
        Arguments:
            in_channels (int): number of channels of the input feature
        """
        super(FCOSHead, self).__init__()
        # TODO: Implement the sigmoid version first.
        num_classes = cfg.MODEL.FCOS.NUM_CLASSES - 1

        cls_tower = []
        bbox_tower = []
        for i in range(cfg.MODEL.FCOS.NUM_CONVS):
            cls_tower.append(
                nn.Conv2d(
                    in_channels,
                    in_channels,
                    kernel_size=3,
                    stride=1,
                    padding=1
                )
            )
            cls_tower.append(nn.GroupNorm(32, in_channels))
            cls_tower.append(nn.ReLU())
            bbox_tower.append(
                nn.Conv2d(
                    in_channels,
                    in_channels,
                    kernel_size=3,
                    stride=1,
                    padding=1
                )
            )
            bbox_tower.append(nn.GroupNorm(32, in_channels))
            bbox_tower.append(nn.ReLU())

        self.add_module('cls_tower', nn.Sequential(*cls_tower))
        self.add_module('bbox_tower', nn.Sequential(*bbox_tower))
        self.cls_logits = nn.Conv2d(
            in_channels, num_classes, kernel_size=3, stride=1,
            padding=1
        )
        self.bbox_pred = nn.Conv2d(
            in_channels, 4, kernel_size=3, stride=1,
            padding=1
        )
        self.centerness = nn.Conv2d(
            in_channels, 1, kernel_size=3, stride=1,
            padding=1
        )

        # initialization
        for modules in [self.cls_tower, self.bbox_tower,
                        self.cls_logits, self.bbox_pred,
                        self.centerness]:
            for l in modules.modules():
                if isinstance(l, nn.Conv2d):
                    torch.nn.init.normal_(l.weight, std=0.01)
                    torch.nn.init.constant_(l.bias, 0)

        # initialize the bias for focal loss
        prior_prob = cfg.MODEL.FCOS.PRIOR_PROB
        bias_value = -math.log((1 - prior_prob) / prior_prob)
        torch.nn.init.constant_(self.cls_logits.bias, bias_value)

        self.scales = nn.ModuleList([Scale(init_value=1.0) for _ in range(5)])
def __init__ ( self , cfg , in_channels ) : super ( FCOSHead , self ) . __init__ ( ) # TODO: Implement the sigmoid version first. num_classes = cfg . MODEL . FCOS . NUM_CLASSES - 1 cls_tower = [ ] bbox_tower = [ ] for i in range ( cfg . MODEL . FCOS . NUM_CONVS ) : cls_tower . append ( nn . Conv2d ( in_channels , in_channels , kernel_size = 3 , stride = 1 , padding = 1 ) ) cls_tower . append ( nn . GroupNorm ( 32 , in_channels ) ) cls_tower . append ( nn . ReLU ( ) ) bbox_tower . append ( nn . Conv2d ( in_channels , in_channels , kernel_size = 3 , stride = 1 , padding = 1 ) ) bbox_tower . append ( nn . GroupNorm ( 32 , in_channels ) ) bbox_tower . append ( nn . ReLU ( ) ) self . add_module ( 'cls_tower' , nn . Sequential ( * cls_tower ) ) self . add_module ( 'bbox_tower' , nn . Sequential ( * bbox_tower ) ) self . cls_logits = nn . Conv2d ( in_channels , num_classes , kernel_size = 3 , stride = 1 , padding = 1 ) self . bbox_pred = nn . Conv2d ( in_channels , 4 , kernel_size = 3 , stride = 1 , padding = 1 ) self . centerness = nn . Conv2d ( in_channels , 1 , kernel_size = 3 , stride = 1 , padding = 1 ) # initialization for modules in [ self . cls_tower , self . bbox_tower , self . cls_logits , self . bbox_pred , self . centerness ] : for l in modules . modules ( ) : if isinstance ( l , nn . Conv2d ) : torch . nn . init . normal_ ( l . weight , std = 0.01 ) torch . nn . init . constant_ ( l . bias , 0 ) # initialize the bias for focal loss prior_prob = cfg . MODEL . FCOS . PRIOR_PROB bias_value = - math . log ( ( 1 - prior_prob ) / prior_prob ) torch . nn . init . constant_ ( self . cls_logits . bias , bias_value ) self . scales = nn . ModuleList ( [ Scale ( init_value = 1.0 ) for _ in range ( 5 ) ] )