nn.MaxPool2d在处理池化操作时，有两个关键参数：ceil_mode和padding，它们在处理边界时有所不同。默认情况下，ceil_mode为False，意味着窗口超出边界时会直接舍弃。如果设置ceil_mode为True，窗口可以超出但不超过一半，这相当于在边缘处做了半步的填充。而padding则是直接在输入数据周围添加指定数量的像...
PyTorch中的MaxPool(最大池化)有一个属性:ceil_mode,默认为False(地板模式),为True时是天花板模式。
# coding:utf-8importtorchimporttorch.nnasnnfromtorch.autogradimportVariableclassNet(nn.Module):def__init__(self):super(Net, self).__init__() self.maxp = nn.MaxPool2d(kernel_size=2, ceil_mode=False)defforward(self, x): x = self.maxp(x)returnx square_size =6inputs = torch.randn...
- 如果 n 是偶数,那么 ceil_mode 的作用就可以体现了。假设n=4,那么 3/2 = 1,此时,输出不符合我们的要求(因为我们想要的是2x2的分辨率,高宽减半),所以设置 ceil_mode=True,向上取整,就可以得到 2 了;当然我们也可以设置ceil_mode=False(pytorch中默认Flase),padding=1,这样就是 (n+1)/2,对于n=4,就...
For L_in=10, ceil_mode=False: The formula gives L_out=5. That's the case. For L_in=11, ceil_mode=False: The formula gives L_out=6. That's the case. For L_in=10, ceil_mode=True: The formula gives L_out=6. But that's not the case: torch.nn.functional.max_pool1d(torch...
import torch.nn as nn from torch.autograd import Variable class Net(nn.Module):def __init__(self):super(Net, self).__init__()self.maxp = nn.MaxPool2d(kernel_size=2, ceil_mode=False)def forward(self, x):x = self.maxp(x)return x square_size = 6 inputs = torch.randn(1, 1...
// If global_pooling then it will pool over the size of the bottom by doing// kernel_h = bottom->height and kernel_w = bottom->widthoptionalboolglobal_pooling =12[default=false]; +// Specify floor/ceil mode+ optionalboolceil_mode =13[default=true];// 为pooling层添加参数,这样可以在ne...
ceil_mode or false self.padW = self.padW or 0 self.padH = self.padH or 0 input.nn.SpatialMaxPooling_updateOutput(self, input) return self.output end @@ -34,6 +52,12 @@ function SpatialMaxPooling:empty() end function SpatialMaxPooling:__tostring__() return string.format('%s(%d,...
test_loader = DataLoader(dataset=happy_dataset, batch_size=4, shuffle=True, num_workers=0, drop_last=False) ''' class Module(nn.Module): def __init__(self): super(Module, self).__init__() # 父类继承 self.maxpool1 = MaxPool2d(kernel_size=3, ceil_mode=True) # kernel_size=3表...
The same issue occurs when ceil_mode=False. Are we sure this is an issue with the computation and not just a documentation issue? Collaborator Guobing-Chen commented Nov 15, 2022 import torch m = torch.nn.MaxPool1d(kernel_size=4, stride=1, padding=0, dilation=1, ceil_mode=True) inp...