In short, nn.Sequential() packs a series of operations into , which could include Conv2d(), ReLU(), Maxpool2d(), etc., which could be packaged to be invoked at any point, but would be a black box, which would be invoked at forward().

extract part of the AlexNet code to understand sequential:

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),
            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),
        )
        ......
        
    def forward(self, x):
        x = self.features(x)
        ......
        return x

init__, self. Features = nn.Sequential(…)

in forward() just use self.features(x) to