AlexNet3D.py

import torch.nn as nn

class AlexNet(nn.Module):

    def __init__(self, num_classes=3):
        super(AlexNet, self).__init__()
        self.features = nn.Sequential(
            nn.Conv3d(1, 64, kernel_size=11, stride=4, padding=2),
            nn.ReLU(inplace=True),
            nn.MaxPool3d(kernel_size=3, stride=2),
            nn.Conv3d(64, 192, kernel_size=5, padding=2),
            nn.ReLU(inplace=True),
            nn.MaxPool3d(kernel_size=3, stride=2),
            nn.Conv3d(192, 384, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv3d(384, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv3d(256, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool3d(kernel_size=3, stride=2),
        )
        self.classifier = nn.Sequential(
            nn.Dropout(),
            nn.Linear(256 * 6 * 6 * 6, 4096),
            nn.ReLU(inplace=True),
            nn.Dropout(),
            nn.Linear(4096, 4096),
            nn.ReLU(inplace=True),
            nn.Linear(4096, num_classes),
        )

        self.reset_parameters()

    def reset_parameters(self):
        for weight in self.parameters():
            weight.data.uniform_(-0.1, 0.1)





    def forward(self, x):
        x = self.features(x)
        x = x.view(x.size(0), 256 * 6 * 6 * 6)
        x = self.classifier(x)
        return x