土堆说卷积操作
- 官网
- debug torch版本只有nn 没有nn.functional
- 代码
- 执行结果
B站小土堆视频学习笔记
官网
https://pytorch.org/docs/stable/nn.html#convolution-layers
常用torch.nn, nn是对nn.functional的封装，使函数更易用。
卷积核从输入图像左上角，先向右遍历行，stride为1 挪一个格位置，向右遍历完，向下一格，再从左向右遍历。
卷积核和输入图像对应位置相乘后结果想加，得到右边的输出结果。
stride
padding
debug torch版本只有nn 没有nn.functional
conda activate pytorchconda install pytorch-cpu torchvision-cpu -c pytorch
在当前环境安装pytorch-cpu后，functional函数就可以调用啦
https://www.saoniuhuo.com/question/detail-2646442.html
代码
import torch
from torch.nn import functional as Finput = 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]])kernel = torch.tensor([[1, 2, 1],[0, 1, 0],[2, 1, 0]])input = torch.reshape(input, [1, 1, 5, 5])
kernel = torch.reshape(kernel, [1, 1, 3, 3])
print(input.shape)
print(kernel.shape)
output1 = F.conv2d(input, kernel, stride=1)
print(output1)output2 = F.conv2d(input, kernel, stride=2)
print(output2)
# 默认padding=0
output3 = F.conv2d(input, kernel, stride=1, padding=1)
print(output3)
执行结果
p14_conv.py
torch.Size([1, 1, 5, 5])
torch.Size([1, 1, 3, 3])
tensor([[[[10, 12, 12],[18, 16, 16],[13, 9, 3]]]])
tensor([[[[10, 12],[13, 3]]]])
tensor([[[[ 1, 3, 4, 10, 8],[ 5, 10, 12, 12, 6],[ 7, 18, 16, 16, 8],[11, 13, 9, 3, 4],[14, 13, 9, 7, 4]]]])Process finished with exit code 0
