retinanet.py 文件源码

def __init__(self, mode, anchors=9, classes=80, depth=4,
                 base_activation=F.relu,
                 output_activation=F.sigmoid):
        super(SubNet, self).__init__()
        self.anchors = anchors
        self.classes = classes
        self.depth = depth
        self.base_activation = base_activation
        self.output_activation = output_activation

        self.subnet_base = nn.ModuleList([conv3x3(256, 256, padding=1)
                                          for _ in range(depth)])

        if mode == 'boxes':
            self.subnet_output = conv3x3(256, 4 * self.anchors, padding=1)
        elif mode == 'classes':
            # add an extra dim for confidence
            self.subnet_output = conv3x3(256, (1 + self.classes) * self.anchors, padding=1)

        self._output_layer_init(self.subnet_output.bias.data)