大小为4x4batch_size=5channels=3height=4width=4# 随机生成特征图input_tensor=torch.randn(batch_size,channels,height,width)print("输入特征图的形状:",input_tensor.shape)# 创建全局最大池化层global_max_pool=nn.AdaptiveMaxPool2d(output_size=(1,1))# 进行全局最大池化output_tensor=global_max_pool...
x = layers.GlobalAveragePooling2D()(x) #GAP层 prediction = Dense(10, activation='softmax')(x) #输出层 1. 2. 3. 再看看GAP的代码具体实现: @tf_export('keras.layers.GlobalAveragePooling2D', 'keras.layers.GlobalAvgPool2D') class GlobalAveragePooling2D(GlobalPooling2D): """Global average ...
torch.nn.MaxPool2d()所需要输入的参数可以参考pooling.py中的说明: Args:kernel_size: the size of the window to take a max overstride: the stride of the window. Default value is :attr:`kernel_size`padding: Implicit negative infinity padding to be added on both sidesdilation: a parameter that...
from torch_geometric.nn import TopKPooling from torch_geometric.nn import global_mean_pool as gap, global_max_pool as gmp import torch.nn.functional as F class Net(torch.nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = SAGEConv(embed_dim, 128) self.pool1...
alexnet(pretrained=True) ''' AlexNet( (features): Sequential( (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2)) (1): ReLU(inplace=True) (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False) (3): Conv2d(64, 192, ...
VGGNet网络结构组成如图3.12所示,一共有6个不同的版本,最常用的是VGG16。VGGNet采用了五组卷积与三个全连接层,最后使用Softmax做分类。VGGNet有一个显著的特点:每次经过池化层(maxpool)后特征图的尺寸减小一倍,而通道数则增加一 倍(最后一个池化层除外)。
# define the timm modeldef get_model():model = VisionTransformer(class_token=False,global_pool="avg",img_size=256,embed_dim=1280,num_classes=1024,depth=32,num_heads=16)if fp8_type:swap_linear_with_float8_linear(model, Float8Linear)return mod...
return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode) RuntimeError: "max_pool2d_channels_last" not implemented for 'Half' run the below codes to reproduce the issue: import numpy as np import torch from onnx2torch import convert ...
nn.MaxPool2d(kernel_size=2,stride=2)) self.fc = nn.Linear(7*7*32,num_classes) def forward(self,x): out = self.layer1(x) out = self.layer2(out) out = out.reshape(out.size(0), -1) out = self.fc(out) return out
global_max_pool=nn.AdaptiveMaxPool2d((1,1))# 创建一个自适应最大池化层output_max=global_max_pool(input_tensor)# 进行全局最大池化print("全局最大池化结果形状:",output_max.shape)# 输出结果形状 1. 2. 3. 步骤4: 输出池化结果 最后,我们查看池化后的结果。