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...
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)...
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...
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
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 number of input planes. ...
如题:只需要给定输出特征图的大小就好,其中通道数前后不发生变化。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
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...
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 number of input planes. ...