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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
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實現(xiàn)ResNet50、ResNet101和ResNet152示例_python_腳本之家
self.avgpool = nn.AvgPool2d(7, stride=1) self.fc = nn.Linear(2048,num_classes) for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') elif isinstance(m, nn.BatchNorm2d): nn.init.constant_(m.weight, 1)...
www.dbjr.com.cn/article/1783...htm 2025-5-28
簡單易懂Pytorch實戰(zhàn)實例VGG深度網(wǎng)絡_python_腳本之家
layers += [nn.AvgPool2d(kernel_size=1, stride=1)] return nn.Sequential(*layers) # net = VGG('VGG11') # x = torch.randn(2,3,32,32) # print(net(Variable(x)).size()) 定義訓練過程: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28...
www.dbjr.com.cn/article/1685...htm 2025-5-25
pytorch rpc實現(xiàn)分物理機器實現(xiàn)model parallel的過程詳解_python_腳本之...
nn.AdaptiveAvgPool2d((1, 1)), ).to(self.device) self.fc = nn.Linear(512 * self._block.expansion, num_classes).to(self.device) def forward(self, x_rref): x = x_rref.to_here().to(self.device) with self._lock: out = self.fc(torch.flatten(self.seq(x), 1)) return out.cpu...
www.dbjr.com.cn/python/2849743...htm 2025-6-9
關(guān)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
python生成器和yield關(guān)鍵字(完整代碼)_python_腳本之家
# self.mean = nn.AdaptiveAvgPool2d((1,1)) # self.conv = nn.Conv2d(in_channel,depth,1,1) # self.atrous_block1 = nn.Conv2d(in_channel,depth,1,1) # self.atrous_block6 = nn.Conv2d(in_channel,depth,3,1,padding=6,dilation=6) # self.atrous_block12 = nn.Conv2d(in_channel,dep...
www.dbjr.com.cn/article/2346...htm 2025-6-8
Pytorch實現(xiàn)GoogLeNet的方法_python_腳本之家
self.avgpool = nn.AvgPool2d(8, stride=1) self.linear = nn.Linear(1024, 10) def forward(self, x): out = self.pre_layers(x) out = self.a3(out) out = self.b3(out) out = self.maxpool(out) out = self.a4(out) out = self.b4(out) out = self.c4(out) out = self.d4(out...
www.dbjr.com.cn/article/1679...htm 2025-5-12