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24、手写并验证向量内积实现PyTorch二维卷积


该9步过程可以看作是一个矩阵跟另外一个矩阵的矩阵相乘将每步input拉直,例如左上角第一个深蓝色区域拉为332001312行向量,再将Kernel拉为$(012220012)^T$列向量相乘得到第一个最终结果的左上角第一个数值12.0So,上边九步运算可以视为行数为9的矩阵和列数为9的矩阵进行矩阵乘法,再将相乘的结果reshape为欲得到的结果Other Method实现一个长度为25的内积目前Kernel只是3×3的范围大小,若是将Kernel填充一下,eg.左上角第一幅图,Kernel只有9个数,但是我们可以脑补一下浅蓝色部分都填充为0,每一步都将Kernel填充浅蓝色部分为0,那么,原问题就变为25行向量和25列向量相乘。Coding希望把region_vector都放入到region_matrix中,再将region_matrix与Kernel_mat...

23、手写并验证滑动相乘实现PyTorch二维卷积


蓝色的input_feature 5*5深蓝色小字部分kernel_size 3*3绿色部分out_feature 3*3stride = 1padding = 0channel = 1padding = 1stride = 2底部input_channels = 2顶端绿色为out_channels = 3kernels = 2*3 = 6(倒数第二行)input = input_feature_map # 卷积输入特征图 kernel = conv_layer.weight.data # 卷积核 input = torch.randn(5, 5) # 卷积输入特征图 kernel = torch.randn(3, 3) # 卷积核 bias = torch.randn(1) # 卷积偏置项,默认输出通道数目=1 # Func1 用原始的矩阵运算...

22、Pytorch nn.Conv2d


22、Pytorch nn.Conv2dCLASS torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros', device=None, dtype=None)[ ](https://pytorch.org/docs/stable/_modules/torch/nn/modules/conv.html#Conv2d)torch import torch.nn as nn import torch.nn.functional as F conv_layer = nn.Conv2d(in_channels=1, out_channels=1, kernel_s...

Module


MODULECLASStorch.nn.Module(args, kwargs*)[SOURCE]层、模型的父类Base class for all neural network modules.Your models should also subclass this class.Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes:import torch.nn as nn import torch.nn.functional as F class Model(nn.Module): def __init__(self): ...

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