classtorch.nn.AdaptiveAvgPool2d(output_size) 对输入信号,提供2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。 参数: output_size: 输出信号的尺寸,可以用(H,W)表示H*W的输出,也可以使用耽搁数字H表示H*H大小的输出 # target output size of ...
pool_size):super(SpatialPyramidPooling,self).__init__()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,...
nn.BatchNorm2d(outchannel), nn.ReLU(inplace=True), nn.Conv2d(in_channels=outchannel, out_channels=outchannel, kernel_size=3, stride=1, padding=1, bias=False), nn.BatchNorm2d(outchannel) ) self.right = shortcut def forward(self, x): out = self.left(x) if self.right: residual =...
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=...
#@tab pytorch b5 = nn.Sequential(Inception(832, 256, (160, 320), (32, 128), 128), Inception(832, 384, (192, 384), (48, 128), 128), nn.AdaptiveAvgPool2d((1,1)), nn.Flatten()) net = nn.Sequential(b1, b2, b3, b4, b5, nn.Linear(1024, 10)) #@t...
[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 ---
[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...
内容编码器由几个 2D 卷积层组成,后跟几个ResBlock。它将输入内容图像 x 映射到内容潜码z_x,其代表空间特征映射。 classContentEncoder(nn.Module):def__init__(self,downs,n_res,input_dim,dim,norm,activ,pad_type):super(ContentEncoder,self).__init__()self.model=[]self.model+=[Conv2dBlock(input...
self.avg_pool = nn.AdaptiveAvgPool2d(1) self.fc = nn.Sequential( nn.Linear(in_planes, out_planes // reduction), nn.ReLU(inplace=True), nn.Linear(out_planes // reduction, out_planes), nn.Sigmoid() ) self.out_planes = out_...