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Pytorch平均池化nn.AvgPool2d()使用方法實例_python_腳本之家

avgpool3 = nn.AvgPool2d(kernel_size=3, stride=2, padding=1, ceil_mode=False, count_include_pad=False, divisor_override=2) y3 = avgpool3(x1) print(y3) # 打印結(jié)果 ''' tensor([[[[ 6., 8.], [12., 14.]]]]) ''' 計算過程: 輸出
www.dbjr.com.cn/article/2761...htm 2025-5-28

pytorch torch.nn.AdaptiveAvgPool2d()自適應平均池化函數(shù)詳解_python...

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 number of input planes. Param...
www.dbjr.com.cn/article/1777...htm 2025-6-1

PyTorch詳解經(jīng)典網(wǎng)絡ResNet實現(xiàn)流程_python_腳本之家

nn.AdaptiveAvgPool2d((1,1)), nn.Flatten(), nn.Linear(512,10)) # 觀察模型各層的輸出尺寸 X=torch.rand(size=(1,1,224,224)) forlayerinnet: X=layer(X) print(layer.__class__.__name__,'output shape:\t', X.shape) 輸出:
www.dbjr.com.cn/article/2470...htm 2025-6-7

YOLOv5改進教程之添加注意力機制_python_腳本之家

self.avgpool=nn.AdaptiveAvgPool2d(1) self.l1=nn.Linear(c1, c1//r, bias=False) self.relu=nn.ReLU(inplace=True) self.l2=nn.Linear(c1//r, c1, bias=False) self.sig=nn.Sigmoid() defforward(self, x): b, c, _, _=x.size() y=self.avgpool(x).view(b, c) y=self.l1(y) y...
www.dbjr.com.cn/article/2530...htm 2025-5-29

Pytorch搭建SRGAN平臺提升圖片超分辨率_python_腳本之家

nn.Conv2d(256, 512, kernel_size=3, padding=1), nn.BatchNorm2d(512), nn.LeakyReLU(0.2), nn.Conv2d(512, 512, kernel_size=3, stride=2, padding=1), nn.BatchNorm2d(512), nn.LeakyReLU(0.2), nn.AdaptiveAvgPool2d(1), nn.Conv2d(512, 1024, kernel_size=1), nn.LeakyReLU(0.2), ...
www.dbjr.com.cn/article/2465...htm 2025-6-1

pytorch SENet實現(xiàn)案例_python_腳本之家

self.avg_pool = nn.AdaptiveAvgPool2d(1) # 全局自適應池化 self.fc = nn.Sequential( nn.Linear(ch_in, ch_in // reduction, bias=False), nn.ReLU(inplace=True), nn.Linear(ch_in // reduction, ch_in, bias=False), nn.Sigmoid() ) def forward(self, x): b, c, _, _ = x.size(...
www.dbjr.com.cn/article/1894...htm 2025-5-27

pytorch1.60 torch.nn在pycharm中無法自動智能提示的解決_python_腳本之...

from .pooling import AdaptiveAvgPool1d as AdaptiveAvgPool1d, AdaptiveAvgPool2d as AdaptiveAvgPool2d, \ AdaptiveAvgPool3d as AdaptiveAvgPool3d, AdaptiveMaxPool1d as AdaptiveMaxPool1d, \ AdaptiveMaxPool2d as AdaptiveMaxPool2d, AdaptiveMaxPool3d as AdaptiveMaxPool3d, AvgPool1d as AvgPool1d, \ Av...
www.dbjr.com.cn/python/316373c...htm 2025-5-27

關于PyTorch源碼解讀之torchvision.models_python_腳本之家

self.avgpool = nn.AvgPool2d(7, stride=1) self.fc = nn.Linear(512 * block.expansion, num_classes) for m in self.modules(): if isinstance(m, nn.Conv2d): n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels m.weight.data.normal_(0, math.sqrt(2. / n)) elif isinsta...
www.dbjr.com.cn/article/1678...htm 2025-5-20

Pytorch上下采樣函數(shù)--interpolate用法_python_腳本之家

return adaptive_avg_pool1d(input, _output_size(1)) elif input.dim() == 4 and mode == 'area': return adaptive_avg_pool2d(input, _output_size(2)) elif input.dim() == 5 and mode == 'area': return adaptive_avg_pool3d(input, _output_size(3)) elif input.dim() == 3 and mode...
www.dbjr.com.cn/article/1902...htm 2025-6-1

pytorch教程resnet.py的實現(xiàn)文件源碼分析_python_腳本之家

self.avgpool = nn.AvgPool2d(7, stride=1) self.fc = nn.Linear(512 * block.expansion, num_classes) for m in self.modules(): if isinstance(m, nn.Conv2d): n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels m.weight.data.normal_(0, math.sqrt(2. / n)) elif isinsta...
www.dbjr.com.cn/article/2221...htm 2025-5-30