函数语言格式: nn.AdaptiveAvgPool2d(output_size) 2.参数解释 output_size:指定输出固定尺寸 3.具体代码 import torch import torch.nnas nn m = nn.AdaptiveAvgPool2d((5,1)) m1 = nn.AdaptiveAvgPool2d((None,5)) m2 = nn.AdaptiveAvgPool2d(1)input= torch.randn(2,64,8,9) output =m(input)...
pytorch中的F.avg_pool2d(),input是维度是4维如[2,2,4,4],表示这里批量数是2也就是两张图像,这里应该是有通道(feature map)数量是2,图像是size是4*4的.核size是(2,2)步长是(2,2)表示被核覆盖的数取平均,横向纵向的步长都是2.那么核是二维的,所以取均值时也是覆盖二维取的。输出中第一个1.5的计算...
1.函数语法格式和作用作用: 自适应平均池化,指定输出(H,W) 函数语言格式:nn.AdaptiveAvgPool2d(output_size) 2.参数解释output_size:指定输出固定尺寸3.具体代码
pytorchAvgPool2d函数使⽤详解 我就废话不多说了，直接上代码吧！import torch import torch.nn as nn import torch.nn.functional as F from torch.autograd import Variable import numpy as np input = Variable(torch.Tensor([[[1, 3, 3, 4, 5, 6, 7], [1, 2, 3, 4, 5, 6, 7]], [[1...
[ 0.0243, -0.2986]]]) >>>0.6574+1.5219+2.7337-0.15614.7569>>>4.7569/41.189225>>> 看完了这篇文章,相信你对“pytorch中torch.nn.AdaptiveAvgPool2d()自适应平均池化函数怎么用”有了一定的了解,如果想了解更多相关知识,欢迎关注亿速云行业资讯频道,感谢各位的阅读!
具体如下: AdaptiveAvgPool2d CLASStorch.nn.AdaptiveAvgPool2d(output_size)[SOURCE] Applies a 2D adaptive average pooling over an input signal composed of several input planes. The output is of size H x W, for any input size. The number of output features is equal to the nu...
具体如下: AdaptiveAvgPool2d CLASStorch.nn.AdaptiveAvgPool2d(output_size)[SOURCE] Applies a 2D adaptive average pooling over an input signal composed of several input planes. The output is of size H x W, for any input size. The number of output features is equal to the nu...
[[ 0.5121, 0.1827], [ 0.0243, -0.2986]]]) >>> 0.6574+1.5219+2.7337-0.1561 4.7569 >>> 4.7569/4 1.189225 >>> 以上这篇pytorch torch.nn.AdaptiveAvgPool2d()自适应平均池化函数详解就是小编分享给大家的全部内容了,希望能给大家一个参考,也希望大家多多支持。