Class AlexNet

CV/Pytorch_classification/AlexNet/model.py:5–50

class AlexNet(nn.Module):
    def __init__(self, num_classes = 1000, init_weights = False):
        super(AlexNet, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 48, kernel_size = 11, stride = 4, padding = 2),  #padding也可以是tuple:(1,2)，
            # 1表示上下方各补一行0，2代表左右两侧各补2列0
            nn.ReLU(inplace = True),
            nn.MaxPool2d(kernel_size = 3, stride = 2),
            nn.Conv2d(48, 128, kernel_size = 5, padding = 2),
            nn.ReLU(inplace = True),
            nn.MaxPool2d(kernel_size = 3, stride = 2),
            nn.Conv2d(128, 192, kernel_size = 3, padding = 1),
            nn.ReLU(inplace = True),
            nn.Conv2d(192, 192, kernel_size = 3, padding = 1),
            nn.ReLU(inplace = True),
            nn.Conv2d(192, 128, kernel_size = 3, padding = 1),
            nn.ReLU(inplace = True),
            nn.MaxPool2d(kernel_size = 3, stride = 2),
        )
        self.classifier = nn.Sequential(
            nn.Dropout(p = 0.5),
            nn.Linear(128 * 6 * 6, 2048),
            nn.ReLU(inplace = True),
            nn.Dropout(p = 0.5),
            nn.Linear(2048, 2048),
            nn.ReLU(inplace = True),
            nn.Linear(2048, num_classes),
        )
        if init_weights:
            self._initialize_weights()

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

    def _initialize_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode = 'fan_out', nonlinearity = 'relu')
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)
                elif isinstance(m, nn.Linear):
                    nn.init.normal_(m.weight, 0, 0.01)
                    nn.init.constant_(m.bias, 0)