22汇聚层

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import torch
from torch import nn
from d2l import torch as d2l

# 输出为输入中每个区域的最大值或平均值
def pool2d(X, pool_size, mode='max'):
    p_h, p_w = pool_size
    Y = torch.zeros((X.shape[0] - p_h + 1, X.shape[1] - p_w + 1))
    for i in range(Y.shape[0]):
        for j in range(Y.shape[1]):
            if mode == 'max':
                Y[i, j] = X[i: i + p_h, j: j + p_w].max()
            elif mode == 'avg':
                Y[i, j] = X[i: i + p_h, j: j + p_w].mean()
    return Y

X = torch.tensor([[0.0, 1.0, 2.0],
                  [3.0, 4.0, 5.0],
                  [6.0, 7.0, 8.0]])
# print(f'pool2d(X, (2, 2)) = {pool2d(X, (2, 2))}')
"""
pool2d(X, (2, 2)) = tensor([[4., 5.],
                            [7., 8.]])
"""
# print('pool2d(X, (2, 2), ''avg'') = ', pool2d(X, (2, 2), 'avg'))
"""
pool2d(X, (2, 2), avg) =  tensor([[2., 3.],
                                  [5., 6.]])

"""

X = torch.arange(16, dtype=torch.float32).reshape((1, 1, 4, 4))
"""
tensor([[[[ 0.,  1.,  2.,  3.],
          [ 4.,  5.,  6.,  7.],
          [ 8.,  9., 10., 11.],
          [12., 13., 14., 15.]]]])
"""
# 步幅和池化窗口大小相同
pool2d = nn.MaxPool2d(3)
# print(pool2d(X))
"""tensor([[[[10.]]]])"""

pool2d = nn.MaxPool2d(3, padding=1, stride=2)
# print(pool2d(X))
"""
tensor([[[[ 5.,  7.],
          [13., 15.]]]])
"""

pool2d = nn.MaxPool2d((2, 3), stride=(2, 3), padding=(0, 1))
# print(pool2d(X))
"""
tensor([[[[ 5.,  7.],
          [13., 15.]]]])
"""

# 池化在每个输入通道上单独运算
# print(X.shape)
# X = torch.stack((X, X+1), 1)
# print(X)
# print(X.shape)
X = torch.cat((X, X + 1), 1)
# print(X.shape)
"""
torch.Size([1, 1, 4, 4])
tensor([[[[ 0.,  1.,  2.,  3.],
          [ 4.,  5.,  6.,  7.],
          [ 8.,  9., 10., 11.],
          [12., 13., 14., 15.]],

         [[ 1.,  2.,  3.,  4.],
          [ 5.,  6.,  7.,  8.],
          [ 9., 10., 11., 12.],
          [13., 14., 15., 16.]]]])
torch.Size([1, 2, 4, 4])

torch.Size([1, 1, 4, 4])
tensor([[[[[ 0.,  1.,  2.,  3.],
           [ 4.,  5.,  6.,  7.],
           [ 8.,  9., 10., 11.],
           [12., 13., 14., 15.]]],


         [[[ 1.,  2.,  3.,  4.],
           [ 5.,  6.,  7.,  8.],
           [ 9., 10., 11., 12.],
           [13., 14., 15., 16.]]]]])
torch.Size([1, 2, 1, 4, 4])
"""

pool2d = nn.MaxPool2d(3, padding=1, stride=2)
# print(pool2d(X))
"""
tensor([[[[ 5.,  7.],
          [13., 15.]],

         [[ 6.,  8.],
          [14., 16.]]]])
"""