在AlexNet模型中，经过卷积操作之后得到的输出被称为“卷积层特征图”，即卷积层的输出是一个三维张量，其中第一维表示卷积核的数量，第二维和第三维表示特征图的行数和列数。

如果使用Pytorch实现AlexNet模型，可以通过以下代码获取第一个卷积层的输出：

import torch.nn as nn
import torch

# 定义AlexNet模型
class AlexNet(nn.Module):
    def __init__(self, num_classes=1000):
        super(AlexNet, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),
            nn.Conv2d(64, 192, kernel_size=5, padding=2),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),
            nn.Conv2d(192, 384, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(384, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),
        )
        self.avgpool = nn.AdaptiveAvgPool2d((6, 6))
        self.classifier = nn.Sequential(
            nn.Dropout(),
            nn.Linear(256 * 6 * 6, 4096),
            nn.ReLU(inplace=True),
            nn.Dropout(),
            nn.Linear(4096, 4096),
            nn.ReLU(inplace=True),
            nn.Linear(4096, num_classes),
        )

    def forward(self, x):
        x = self.features(x)
        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.classifier(x)
        return x

# 加载预训练模型
model = AlexNet()
model_path = "model/alexnet-owt-7be5be79.pth" # 预训练模型路径
pretrained_dict = torch.load(model_path)
model_dict = model.state_dict()
pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)

# 获取第一个卷积层的输出
input = torch.randn(1, 3, 224, 224) # 输入数据，shape为[1, 3, 224, 224]
conv1 = nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2)
x = conv1(input)
print(x.shape) # 输出特征图的shape