接下来,我们来了解一下torch.nn.adaptive_max_pool2d函数的具体实现原理。该函数的主要作用是对输入特征图进行自适应最大池化操作,将输入特征图进行降维处理。下面是使用adaptive_max_pool2d函数的示例代码: python import torch import torch.nn as nn #创建输入特征图 input = torch.randn(1, 64, 32, 32) #...
在PyTorch中,torch.nn.adaptivemaxpool2d函数可以通过以下方式调用: python output = torch.nn.functional.adaptive_max_pool2d(input, output_size) 其中,input是输入的特征图,output_size是一个元组,用于指定输出的尺寸。output是经过自适应最大池化操作后的输出。 自适应最大池化的原理是什么? 自适应最大池化的原...
m = nn.AdaptiveMaxPool2d((None, 7)) input = torch.randn(1, 64, 10, 9) output = m(input) output.size() torch.Size([1, 64, 10, 7]) Adaptive Pooling特殊性在于,输出张量的大小都是给定的output_size output_sizeoutput_size。例如输入张量大小为(1, 64, 8, 9),设定输出大小为(5,7),...
m = nn.AdaptiveMaxPool2d((None, 7)) input = torch.randn(1, 64, 10, 9) output = m(input) output.size() torch.Size([1, 64, 10, 7]) Adaptive Pooling特殊性在于,输出张量的大小都是给定的output_size output_sizeoutput_size。例如输入张量大小为(1, 64, 8, 9),设定输出大小为(5,7),...
在PyTorch中,全局池化可以通过torch.nn.functional模块中的adaptive_avg_pool2d和adaptive_max_pool2d函数实现全局平均池化和全局最大池化,也可以通过nn.AdaptiveAvgPool2d和nn.AdaptiveMaxPool2d类来实现。 3. PyTorch代码示例 以下是一个使用全局平均池化的简单PyTorch代码示例: python import torch import torch.nn as...
torch.nn.adaptivemaxpool2d原理 Adaptive Max Pooling is a popular technique used in deep learning for reducing the dimensions of the input feature maps while preserving the most relevant information. In this article, we will delve into the principles behindAdaptive Max Pooling and explore the step-...
torch.nn.adaptivemaxpool2d原理-回复 AdaptiveMaxPool2D is a module in the PyTorch library that performs adaptive max pooling over an input signal composed of several input planes. In this article, we will explore the principles behind AdaptiveMaxPool2D anddiscuss its step-by-step functioning. 1....
(input)>>># target output size of 7x7 (square)>>> m = nn.AdaptiveAvgPool2d(7)>>> input = torch.randn(1, 64, 10, 9)>>> output = m(input)>>># target output size of 10x7>>> m = nn.AdaptiveMaxPool2d((None,7))>>> input = torch.randn(1, 64, 10, 9)>>> output = ...
class torch.nn.AdaptiveMaxPool2d(output_size, return_indices=False) # 输入输出的HW不变 ③ class torch.nn.BatchNorm2d(num_features, eps=1e-05, momentum=0.1, affine=True) 在每一个小批量(mini-batch)数据中,计算输入各个通道的均值和标准差。gamma与beta是可学习的大小为C的参数向量(C为输入大小)...
torch.nn.functionaltorch.nn.functional.adaptive_avg_pool1d()torch.nn.functional.adaptive_avg_pool2d()torch.nn.functional.adaptive_avg_pool3d()torch.nn.functional.adaptive_max_pool1d()torch.nn.functional.adaptive_max_pool2d()torch.nn.functional.adaptive_max_pool3d()torch.nn.functional.affine_grid...