PyTorch 学习笔记（七）：卷积神经网络案例分析——AlexNet、VGGNet、GoogLeNet、ResNet

最新推荐文章于 2022-11-06 00:08:19 发布

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最新推荐文章于 2022-11-06 00:08:19 发布 · 2.6k 阅读

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文章标签：

#AlexNet #VGGNet #GoogLeNet #ResNet

我写这篇的目的主要是想熟悉一下PyTorch搭建模型的方法。

一. AlexNet

在这里插入图片描述
五个卷积层加3个全连接层，话不多说，直接上代码：

import torch
from torch import nn
from torchstat import stat
class AlexNet(nn.Module):
    def __init__(self, num_classes):
        super(AlexNet, self).__init__()      # b, 3, 224, 224
        self.features = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),   # b, 64, 55, 55
            nn.ReLU(True),
            nn.MaxPool2d(kernel_size=3, stride=2),    # b, 64, 27, 27

            nn.Conv2d(64, 192, kernel_size=5, padding=2),      # b, 192, 27, 27
            nn.ReLU(True),
            nn.MaxPool2d(kernel_size=3, stride=2),   # b, 192, 13, 13

            nn.Conv2d(192, 384, kernel_size=3, padding=1),   # b, 384, 13, 13
            nn.ReLU(True),
            nn.Conv2d(384, 256, kernel_size=3, padding=1),   # b, 256, 13, 13
            nn.ReLU(True),
            nn.Conv2d(256, 256, kernel_size=3, padding=1),   # b, 256, 13, 13
            nn.ReLU(True),
            nn.MaxPool2d(kernel_size=3, stride=2))    # b, 256, 6, 6
        self.classifier = nn.Sequential(
            nn.Dropout(),
            nn.Linear(256*6*6, 4096),
            nn.ReLU(True),
            nn.Dropout(),
            nn.Linear(4096, 4096),
            nn.ReLU(True),
            nn.Linear(4096, num_classes))
    def forward(self, x):
        x = self.features(x)
        print(x.size())
        x = x.view(x.size(0), 256*6*6)
        x = self.classifier(x)
        return x

model = AlexNet(10)
stat(model, (3, 224, 224))

使用stat模块对模型参数量和计算量进行估计，顺便也验证了模型是否正确，运行结果：

torch.Size([1, 256, 6, 6])
[MAdd]: Dropout is not supported!
[Flops]: Dropout is not supported!
[Memory]: Dropout is not supported!
[MAdd]: Dropout is not supported!
[Flops]: Dropout is not supported!
[Memory]: Dropout is not supported!
        module name  input shape output shape      params memory(MB)             MAdd          Flops   MemRead(B)  MemWrite(B) duration[%]    MemR+W(B)
0        features.0    3 224 224   64  55  55     23296.0       0.74    140,553,600.0   70,470,400.0     695296.0     774400.0      55.56%    1469696.0
1        features.1   64  55  55   64  55  55         0.0       0.74        193,600.0      193,600.0     774400.0     774400.0       0.00%    1548800.0
2        features.2   64  55  55   64  27  27         0.0       0.18        373,248.0      193,600.0     774400.0     186624.0       5.57%     961024.0
3        features.3   64  27  27  192  27  27    307392.0       0.53    447,897,600.0  224,088,768.0    1416192.0     559872.0      22.21%    1976064.0
4        features.4  192  27  27  192  27  27         0.0       0.53        139,968.0      139,968.0     559872.0     559872.0       0.00%    1119744.0
5        features.5  192  27  27  192  13  13         0.0       0.12        259,584.0      139,968.0     559872.0     129792.0       0.00%     689664.0
6        features.6  192  13  13  384  13  13    663936.0       0.25    224,280,576.0  112,205,184.0    2785536.0     259584.0       0.00%    3045120.0
7        features.7  384  13  13  384  13  13         0.0       0.25         64,896.0       64,896.0     259584.0     259584.0       0.00%     519168.0
8        features.8  384  13  13  256  13  13    884992.0       0.17    299,040,768.0  149,563,648.0    3799552.0     173056.0       5.56%    3972608.0
9        features.9  256  13  13  256  13  13         0.0       0.17         43,264.0       43,264.0     173056.0     173056.0       0.00%     346112.0
10      features.10  256  13  13  256  13  13    590080.0       0.17    199,360,512.0   99,723,520.0    2533376.0     173056.0       0.00%    2706432.0
11      features.11  256  13  13  2