Modify YOLOv3 backbone from DarkNet to AdderNet

How to correctly modify https://github.com/eriklindernoren/PyTorch-YOLOv3 to use https://github.com/huawei-noah/AdderNet ?

The following colab ipynb notebook is what I have so far with the helps of others:

https://colab.research.google.com/drive/1VCafwykgNKAO6144LssBFFy0TmruDNSE#scrollTo=W3e-WcVxnKfs

How to solve the error on this models.py file ?

Namespace(batch_size=8, class_path='data/coco.names', conf_thres=0.001, data_config='config/coco.data', img_size=416, iou_thres=0.5, model_def='config/yolov3.cfg', n_cpu=8, nms_thres=0.5, weights_path='weights/yolov3.weights')
Traceback (most recent call last):
  File "test.py", line 84, in <module>
    model.load_darknet_weights(opt.weights_path)
  File "/content/PyTorch-YOLOv3/models.py", line 321, in load_darknet_weights
    num_w = conv_layer.weight.numel()
  File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 576, in __getattr__
    type(self).__name__, name))
AttributeError: 'adder2d' object has no attribute 'weight'

[HantingChen]

‘adder2d’ object has no attribute ‘weight’, you may change the definition of self.adder with self.weight in class adder2d.

[buttercutter]

@summeryumyee

For your question in https://github.com/eriklindernoren/PyTorch-YOLOv3/issues/482#issuecomment-615583101, you could use modification in the following class :

class adder2d(nn.Module):

    def __init__(self,input_channel,output_channel,kernel_size, stride=1, padding=0, bias = False):
        super(adder2d, self).__init__()
        self.stride = stride
        self.padding = padding
        self.input_channel = input_channel
        self.output_channel = output_channel
        self.kernel_size = kernel_size
        self.adder = torch.nn.Parameter(nn.init.normal_(torch.randn(output_channel,input_channel,kernel_size,kernel_size)))
        self.bias = bias
        if bias:
            self.b = torch.nn.Parameter(nn.init.uniform_(torch.zeros(output_channel)))

    def forward(self, x):
        output = adder2d_function(x,self.adder, self.stride, self.padding)
        if self.bias:
            output += self.b.unsqueeze(0).unsqueeze(2).unsqueeze(3)
        
        return output