池化层

MaxPool2d
在这里插入图片描述

kernel_size：窗口的大小以达到最大值
stride：窗口的步幅。默认值为kernel_size
padding：要在两侧添加隐式零填充
dilation：控制窗口中元素步幅的参数
return_indices：如果True，将返回最大索引和输出。
ceil_mode：当为 True 时，将使用ceil而不是floor来计算输出形状

import torch
from torch import nn
from torch.nn import MaxPool2d

input = torch.tensor([[1, 2, 0, 3, 1],
                      [0, 1, 2, 3, 1],
                      [1, 2, 1, 0, 0],
                      [5, 2, 3, 1, 1],
                      [2, 1, 0, 1, 1]], dtype = torch.float32)
input = torch.reshape(input, (-1, 1, 5, 5))
print(input.shape)


class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.maxpool1 = MaxPool2d(kernel_size = 3, ceil_mode = True)

    def forward(self, input):
        output = self.maxpool1(input)
        return output


model = Model()
output = model(input)
print(output)

输出结果：
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import torch
from torch import nn
from torch.nn import MaxPool2d

input = torch.tensor([[1, 2, 0, 3, 1],
                      [0, 1, 2, 3, 1],
                      [1, 2, 1, 0, 0],
                      [5, 2, 3, 1, 1],
                      [2, 1, 0, 1, 1]], dtype = torch.float32)
input = torch.reshape(input, (-1, 1, 5, 5))
print(input.shape)


class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.maxpool1 = MaxPool2d(kernel_size = 3, ceil_mode = False)

    def forward(self, input):
        output = self.maxpool1(input)
        return output


model = Model()
output = model(input)
print(output)