In [1]:
Copied!
import torch
from torch import nn
def pool2d(X, pool_size, mode='max'):
X = X.float()
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
import torch
from torch import nn
def pool2d(X, pool_size, mode='max'):
X = X.float()
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
In [2]:
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X = torch.tensor([[0, 1, 2], [3, 4, 5], [6, 7, 8]])
pool2d(X, (2, 2))
X = torch.tensor([[0, 1, 2], [3, 4, 5], [6, 7, 8]])
pool2d(X, (2, 2))
Out[2]:
tensor([[4., 5.],
[7., 8.]])
填充与步幅¶
In [3]:
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X = torch.arange(16, dtype=torch.float).view((1, 1, 4, 4))
X
X = torch.arange(16, dtype=torch.float).view((1, 1, 4, 4))
X
Out[3]:
tensor([[[[ 0., 1., 2., 3.],
[ 4., 5., 6., 7.],
[ 8., 9., 10., 11.],
[12., 13., 14., 15.]]]])
In [4]:
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pool2d = nn.MaxPool2d((2, 4), padding=(1, 2), stride=(2, 3))
pool2d(X)
pool2d = nn.MaxPool2d((2, 4), padding=(1, 2), stride=(2, 3))
pool2d(X)
Out[4]:
tensor([[[[ 1., 3.],
[ 9., 11.],
[13., 15.]]]])
多通道¶
In [5]:
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# 池化前后通道数不变
X = torch.cat((X, X + 1), dim=1)
pool2d = nn.MaxPool2d(3, padding=1, stride=2)
pool2d(X)
# 池化前后通道数不变
X = torch.cat((X, X + 1), dim=1)
pool2d = nn.MaxPool2d(3, padding=1, stride=2)
pool2d(X)
Out[5]:
tensor([[[[ 5., 7.],
[13., 15.]],
[[ 6., 8.],
[14., 16.]]]])