def __init__()

in GraphAutoEncoder/graphVAESSW.py [0:0]

• 41 lines of code
• 7 McCabe index (conditional complexity)

    def __init__(self, in_channel, out_channel, weight_num,in_point_num,  connection_info, b_Perpt_bias = True,  residual_rate = 0.0): #layer_info_lst= [(point_num, feature_dim)]
        super(LASMConvssw, self).__init__()

        self.relu = nn.ELU()        

        self.in_channel = in_channel
        self.out_channel = out_channel
        self.weight_num = weight_num 
        self.in_point_num = in_point_num
        
        
        out_point_num = connection_info.shape[0]
        self.out_point_num = out_point_num 
                
        neighbor_num_lst = torch.from_numpy(connection_info[:,0].astype(np.float32)).float() #out_point_num*1
        self.register_buffer("neighbor_num_lst", neighbor_num_lst)
        
        neighbor_id_dist_lstlst = connection_info[:, 1:] #out_point_num*(max_neighbor_num*2)
        neighbor_id_lstlst = neighbor_id_dist_lstlst.reshape((out_point_num, -1,2))[:,:,0] #out_point_num*max_neighbor_num
        
        neighbor_id_lstlst = torch.from_numpy(neighbor_id_lstlst).long()
        self.register_buffer("neighbor_id_lstlst", neighbor_id_lstlst)
        
        max_neighbor_num = neighbor_id_lstlst.shape[1]
        self.max_neighbor_num = max_neighbor_num

        avg_neighbor_num= round(neighbor_num_lst.mean().item())
        self.avg_neighbor_num = avg_neighbor_num
            
            
        ####parameters for conv###############
        weights = nn.Parameter(torch.randn(weight_num, out_channel*in_channel))
        self.register_parameter("weights",weights)
            
        bias = nn.Parameter(torch.zeros(out_channel))
        if b_Perpt_bias:
            bias= nn.Parameter(torch.zeros(out_point_num, out_channel))

        self.register_parameter("bias",bias)
        
        self.residual_rate = residual_rate

        ####parameters for residual###############
        #residual_layer = ""
        if self.residual_rate > 0:
        
            if(out_point_num != in_point_num):        
                p_neighbors = nn.Parameter(torch.randn(out_point_num, max_neighbor_num)/(avg_neighbor_num))
                self.register_parameter("p_neighbors",p_neighbors)

            if(out_channel != in_channel):
                weight_res = torch.randn(1, out_channel*in_channel)                
                weight_res = weight_res/out_channel

                weight_res = nn.Parameter(weight_res)
                self.register_parameter("weight_res",weight_res)


        print ("in_channel", in_channel,\
        	"out_channel",out_channel, \
        	"in_point_num", in_point_num, \
        	"out_point_num", out_point_num, \
        	"weight_num", weight_num,\
            "max_neighbor_num", max_neighbor_num)