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class ModelObj: # 网络对象
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def __init__(self, ObjID, ObjType, ObjLable, ParaString, ObjX, ObjY):
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self.ObjID = ObjID # 图元号
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self.ObjType = ObjType # 图元类别
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self.ObjLable = ObjLable # 对象标签
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self.ParaString = ParaString # 参数字符串
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self.ObjX = ObjX # 对象位置x坐标
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self.ObjY = ObjY # 对象位置y坐标
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class Conv_Class(ModelObj): # 卷积对象
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def __init__(self, ObjID, ObjType, ObjLable, ParaString, ObjX, ObjY):
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super().__init__(ObjID, ObjType, ObjLable, ParaString, ObjX, ObjY)
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self.ConvProc = self.conv_proc # 基本操作函数
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self.SetConvPara = self.setconv_para # 参数设置函数
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def setconv_para(self): # 定义设置卷积参数的函数SetConvPara()
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kernel_h = int(input("请输入卷积核的高度: ")) # 用户输入卷积核的高度
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kernel_w = int(input("请输入卷积核的宽度: ")) # 用户输入卷积核的宽度
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# 用户输入卷积核的值
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kernel = []
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print("请输入卷积核的值：")
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for i in range(kernel_h):
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row = [float(val) for val in input().split()]
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kernel.append(row)
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stride = int(input("请输入步长: ")) # 用户输入步长
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padding = int(input("请输入填充: ")) # 用户输入填充
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# 返回ConvPara参数，这里用一个字典来存储
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ConvPara = {"kernel": kernel, "kernel_h": kernel_h,
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"kernel_w": kernel_w, "stride": stride,
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"padding": padding}
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return ConvPara
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if __name__ == '__main__':
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Conv = Conv_Class("Conv1", 2, "卷积1", [], 250, 330)
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ConvPara = Conv.SetConvPara()
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print(ConvPara)
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