m=F.avg_pool2d(input,(4,4))print(m.size()) torch.Size([10,3,1,1]) 补充:PyTorch中AdaptiveAvgPool函数解析 自适应池化(AdaptiveAvgPool1d): 对输入信号,提供1维的自适应平均池化操作 对于任何输入大小的输入,可以将输出尺寸指定为H*W,但是输入和输出特征的数目不会变化。 torch.nn.AdaptiveAvgPool1d...
m=F.avg_pool2d(input,(4,4))print(m.size()) torch.Size([10,3,1,1]) AI代码助手复制代码 补充:PyTorch中AdaptiveAvgPool函数解析 自适应池化(AdaptiveAvgPool1d): 对输入信号,提供1维的自适应平均池化操作 对于任何输入大小的输入,可以将输出尺寸指定为H*W,但是输入和输出特征的数目不会变化。 torch.n...
pytorch中F.avg_pool1d（）和F.avg_pool2d（）的使⽤ 操作 F.avg_pool1d()数据是三维输⼊ input维度：（batch_size,channels,width）channel可以看成⾼度 kenerl维度：（⼀维:表⽰width的跨度）channel和输⼊的channel⼀致可以认为是矩阵的⾼度 假设kernel_size=2,则每俩列相加求平均，stride默认...
(conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (bn2): BatchN...
()self.pool_size=pool_size# 传入的池化层大小defforward(self,x):batch_size,channel,height,width=x.size()# 初始化一个列表来保存不同池化层的输出output=[]forsizeinself.pool_size:# 使用平均池化pool=F.adaptive_avg_pool2d(x,size)# 进行自适应平均池化output.append(pool.view(batch_size,-1))#...
int pool = 0; if (op == "adaptive_avg_pool2d") { pool = 1; } int adaptive_pooling = 1; const onnx::TensorProto& out_shape_tp = weights[node.input(1)]; std::vector<int> out_shape = get_node_attr_from_input_ai(out_shape_tp); fprintf(pp, " 0=%d", pool); fprintf(pp,...
(dim,2*dim,4,2,1,norm=norm,activation=activ,pad_type=pad_type)]dim*=2foriinrange(downs-2):self.model+=[Conv2dBlock(dim,dim,4,2,1,norm=norm,activation=activ,pad_type=pad_type)]self.model+=[nn.AdaptiveAvgPool2d(1)]self.model+=[nn.Conv2d(dim,latent_dim,1,1,0)]self.model=...
net.add(nn.GlobalAvgPool2D(), nn.Dense(10)) #@tab pytorch net = nn.Sequential(b1, b2, b3, b4, b5, nn.AdaptiveAvgPool2d((1,1)), nn.Flatten(), nn.Linear(512, 10)) #@tab tensorflow # Recall that we define this as a function so we can reuse later an...
[3], intermediate_channels=512, stride=2)self.avgpool = nn.AdaptiveAvgPool2d((1, 1))self.fc = nn.Linear(512 * 4, num_classes)def forward(self, x):x = self.conv1(x)x = self.bn1(x)x = self.relu(x)x = self.maxpool(x)x = self.layer1(x)x = self.layer2(x)x = self...
[1, 2048, 1, 1] 0 AdaptiveAvgPool2D-1 [[1, 2048, 1, 1]] [1, 2048, 1, 1] 0 Linear-1 [[1, 2048]] [1, 100] 204,900 === Total params: 58,500,132 Trainable params: 58,197,284 Non-trainable params: 302,848 ---