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import numpy as np
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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 Pool_Class(ModelObj): # 池化对象
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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.MaxPoolProc = self.pool_proc # 基本操作函数
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self.SetPollPara = self.setpool_para # 参数设置函数
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def pool_proc(self, image, PoolPara):
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pool_mode = PoolPara["pool_mode"]
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pool_size = PoolPara["pool_size"]
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stride = PoolPara["stride"]
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c, h, w = image.shape # 获取输入特征图的高度和宽度
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out_h = int((h - pool_size) / stride) + 1 # 计算输出特征图的高度
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out_w = int((w - pool_size) / stride) + 1 # 计算输出特征图的宽度
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out = np.zeros((c, out_h, out_w)) # 初始化输出特征图为全零数组
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for k in range(c): # 对于输出的每一个位置上计算：
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for i in range(out_h):
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for j in range(out_w):
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window = image[k, i * stride:i * stride + pool_size,
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j * stride:j * stride + pool_size]
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if pool_mode == "max": # 最大池化
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out[k][i][j] = np.max(window)
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elif pool_mode == "avg": # 平均池化
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out[k][i][j] = np.mean(window)
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elif pool_mode == "min": # 最小池化
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out[k][i][j] = np.min(window)
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else: # 无效的池化类型
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raise ValueError("Invalid pooling mode")
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return out # 返回特征图。
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def setpool_para(self): # 定义设置池化参数的函数
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pool_mode = input("请输入池化模式（max/avg/min）: ") # 用户输入池化模式
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pool_size = int(input("请输入池化大小: ")) # 用户输入池化大小
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stride = int(input("请输入步长: ")) # 用户输入步长
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PoolPara = {"pool_mode": pool_mode, "pool_size": pool_size,
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"stride": stride} # 返回PoolPara参数，这里用字典来存储
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return PoolPara # 返回PoolPara参数
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if __name__ == '__main__':
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Pool = Pool_Class("Pool1", 3, "池化1", [], 300, 400)
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PoolPara = Pool.SetPollPara()
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print(PoolPara)
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