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03.padding.py
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29 lines (22 loc) · 913 Bytes
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# -*- coding: utf-8 -*-
"""
@author:XuMing([email protected])
@description:
"""
import torch
from torch import nn
# 定义一个函数来计算卷积层。它对输入和输出做相应的升维和降维
def comp_conv2d(conv2d, X):
# (1, 1)代表批量大小和通道数（“多输入通道和多输出通道”一节将介绍）均为1
X = X.view((1, 1) + X.shape)
Y = conv2d(X)
return Y.view(Y.shape[2:]) # 排除不关心的前两维：批量和通道
# 注意这里是两侧分别填充1行或列，所以在两侧一共填充2行或列
conv2d = nn.Conv2d(in_channels=1, out_channels=1, kernel_size=3, padding=1)
X = torch.rand(8, 8)
print(comp_conv2d(conv2d, X).shape)
# stride
conv2d = nn.Conv2d(1,1,kernel_size=3,padding=1, stride=2)
print(comp_conv2d(conv2d, X).shape)
conv2d = nn.Conv2d(1, 1, kernel_size=(3, 5), padding=(0, 1), stride=(3, 4))
print(comp_conv2d(conv2d, X).shape)